This document summarizes research on using filtered acoustic emission signals to monitor the condition of rolling element bearings. The researchers collected acoustic emission data from both healthy and defective bearings. They applied three active noise cancellation techniques (LMS, EMD, wavelet) to filter the noisy acoustic signals and compared their performance based on SNR and MSE, finding that EMD provided the best filtering. Time, frequency, and time-frequency analyses were then used to analyze the filtered signals and diagnose bearing faults. The analyses clearly showed differences between healthy and defective bearings and could detect different types of defects. The research demonstrates that acoustic emission monitoring combined with noise filtering is effective for rolling element bearing condition monitoring and fault diagnosis.
Iaetsd detection, recognition and localization of partial dischargeIaetsd Iaetsd
Partial discharge (PD) in power transformers can be detected using acoustic emission (AE) sensors. PD produces acoustic waves that can be picked up by external sensors. The document discusses using AE sensors to detect and localize PD sources to improve diagnosis of insulation issues. It describes typical AE measurement systems that use sensors, preamplifiers and software for signal processing. Broadband sensors that detect a wide range of frequencies provide better detection of different types of PD compared to narrowband sensors. Proper acoustic coupling of sensors to the transformer is important for sensitivity. AE methods allow online monitoring of transformers and localization of PD for diagnosis.
ESPRIT Method Enhancement for Real-time Wind Turbine Fault RecognitionIAES-IJPEDS
Early fault diagnosis plays a very important role in the modern energy production systems. The wind turbine machine requires a regular maintenance to guarantee an acceptable lifetime and to minimize production loss. In order to implement a fast, proactive condition monitoring, ESPRIT- TLS method seems the correct choice due to its robustness in improving the frequency and amplitude detection. Nevertheless, it has a very complex computation to implement in real time. To avoid this problem, a Fast- ESPRIT algorithm that combined the IIR band-pass filtering technique, the decimation technique and the original ESPRIT-TLS method were employed to enhance extracting accurately frequencies and their magnitudes from the wind stator current. The proposed algorithm has been evaluated by computer simulations with many fault scenarios. Study results demonstrate the performance of Fast-ESPRIT allowing fast and high resolution harmonics identification with minimum computation time and less memory cost.
Myocardial Infarction is one of the fatal heart diseases. It is essential that a patient is monitored for the early detection of MI. Owing to the newer technology such as wearable sensors which are capable of transmitting wirelessly, this can be done easily. However, there is a need for real-time applications that are able to accurately detect MI non-invasively. This project studies a prospective method by which we can detect MI. Our approach analyses the ECG (electrocardiogram) of a patient in real-time and extracts the ST elevation from each cycle. The ST elevation plays an important part in MI detection. We then use the sequential change point detection algorithm; CUmulative SUM (CUSUM), to detect any deviation in the ST elevation spectrum and to raise an alarm if we find any.
IRJET- Design Simulation and Analysis of Efficient De-Noising of ECG Signals ...IRJET Journal
This document discusses techniques for removing noise from electrocardiogram (ECG) signals, including adaptive filtering algorithms and a patch-based method. It first provides background on ECG signals and sources of noise that can interfere with diagnosis. Adaptive filters like least mean square (LMS) and recursive least squares (RLS) are introduced to update filter coefficients based on the signal environment. Simulation results show an ECG signal contaminated with powerline noise can be effectively filtered using LMS. The document also explores a patch-based nonlocal means method previously used for image denoising and applies it to remove noise from ECG signals.
Performance Study of Various Adaptive filter algorithms for Noise Cancellatio...CSCJournals
Removal of noises from respiratory signal is a classical problem. In recent years, adaptive filtering has become one of the effective and popular approaches for the processing and analysis of the respiratory and other biomedical signals. Adaptive filters permit to detect time varying potentials and to track the dynamic variations of the signals. Besides, they modify their behavior according to the input signal. Therefore, they can detect shape variations in the ensemble and thus they can obtain a better signal estimation. This paper focuses on (i) Model Respiratory signal with second order Auto Regressive process. Then randomly generated noises have been mixed with respiratory signal and nullify these noises using various adaptive filter algorithms (ii) to remove motion artifacts and 50Hz Power line interference from sinusoidal 0.18Hz respiratory signal using various adaptive filter algorithms. At the end of this paper, a performance study has been done between these algorithms based on various step sizes. It has been found that there will be always tradeoff between step sizes and Mean square error.
The Analysis of Aluminium Cantilever Beam with Piezoelectric Material by Chan...IRJET Journal
The document describes a study analyzing an aluminum cantilever beam with piezoelectric material to control vibrations. A finite element model of the beam was created in ANSYS software. Modal analysis was performed to determine the beam's natural frequencies without and with piezoelectric patches placed at different positions along the length. The first three natural frequencies of the bare beam were around 8 Hz, 48 Hz, and 118 Hz. Piezoelectric patches were found to shift the beam's natural frequencies higher. The results aim to validate a method for active vibration control of structures.
Autotuning of pid controller for robot arm and magnet levitation planteSAT Journals
Abstract
One of the most essential work of the control engineer is tuning of controller. Majority of the controller used in industry are of the
PID type. An auto tuning is one of the method of controller tuning in which tuning of the parameters of controller is done
automatically and possibly, without any user interaction expect from initiating the operation. Present study emphasis on the relay
based auto tuning of PID controller. An auto-tuning method is implemented based on a relay experiment to determine the ultimate
gain and the ultimate period, with which the PID parameters are obtained using the Ziegler-Nichols tuning rules. An auto tuning
of robot arm model and magnet levitation model are carried out. Performance of relay based auto tuning on the basis of integral
square error is better than artificial neural network.
Keywords: Relay auto tuning, PID, FOPDT, SOPDT, Integral square error.
A Study of Ball Bearing’s Crack Using Acoustic Signal / Vibration Signal and ...INFOGAIN PUBLICATION
The field of fault diagnostic in rotating machinery is vast, including the diagnosis of items such as rotating shafts, rolling element bearings, couplings, gears and so on. Vibration analysis is the main condition monitoring technique for machinery maintenance. The different types of faults that are observed in these areas and the methods of their diagnosis are accordingly great, including vibration analysis, model-based techniques, and statistical analysis and artificial intelligence techniques. However, they have difficulties with certain applications whose behavior is non-stationary and transient nature.
Iaetsd detection, recognition and localization of partial dischargeIaetsd Iaetsd
Partial discharge (PD) in power transformers can be detected using acoustic emission (AE) sensors. PD produces acoustic waves that can be picked up by external sensors. The document discusses using AE sensors to detect and localize PD sources to improve diagnosis of insulation issues. It describes typical AE measurement systems that use sensors, preamplifiers and software for signal processing. Broadband sensors that detect a wide range of frequencies provide better detection of different types of PD compared to narrowband sensors. Proper acoustic coupling of sensors to the transformer is important for sensitivity. AE methods allow online monitoring of transformers and localization of PD for diagnosis.
ESPRIT Method Enhancement for Real-time Wind Turbine Fault RecognitionIAES-IJPEDS
Early fault diagnosis plays a very important role in the modern energy production systems. The wind turbine machine requires a regular maintenance to guarantee an acceptable lifetime and to minimize production loss. In order to implement a fast, proactive condition monitoring, ESPRIT- TLS method seems the correct choice due to its robustness in improving the frequency and amplitude detection. Nevertheless, it has a very complex computation to implement in real time. To avoid this problem, a Fast- ESPRIT algorithm that combined the IIR band-pass filtering technique, the decimation technique and the original ESPRIT-TLS method were employed to enhance extracting accurately frequencies and their magnitudes from the wind stator current. The proposed algorithm has been evaluated by computer simulations with many fault scenarios. Study results demonstrate the performance of Fast-ESPRIT allowing fast and high resolution harmonics identification with minimum computation time and less memory cost.
Myocardial Infarction is one of the fatal heart diseases. It is essential that a patient is monitored for the early detection of MI. Owing to the newer technology such as wearable sensors which are capable of transmitting wirelessly, this can be done easily. However, there is a need for real-time applications that are able to accurately detect MI non-invasively. This project studies a prospective method by which we can detect MI. Our approach analyses the ECG (electrocardiogram) of a patient in real-time and extracts the ST elevation from each cycle. The ST elevation plays an important part in MI detection. We then use the sequential change point detection algorithm; CUmulative SUM (CUSUM), to detect any deviation in the ST elevation spectrum and to raise an alarm if we find any.
IRJET- Design Simulation and Analysis of Efficient De-Noising of ECG Signals ...IRJET Journal
This document discusses techniques for removing noise from electrocardiogram (ECG) signals, including adaptive filtering algorithms and a patch-based method. It first provides background on ECG signals and sources of noise that can interfere with diagnosis. Adaptive filters like least mean square (LMS) and recursive least squares (RLS) are introduced to update filter coefficients based on the signal environment. Simulation results show an ECG signal contaminated with powerline noise can be effectively filtered using LMS. The document also explores a patch-based nonlocal means method previously used for image denoising and applies it to remove noise from ECG signals.
Performance Study of Various Adaptive filter algorithms for Noise Cancellatio...CSCJournals
Removal of noises from respiratory signal is a classical problem. In recent years, adaptive filtering has become one of the effective and popular approaches for the processing and analysis of the respiratory and other biomedical signals. Adaptive filters permit to detect time varying potentials and to track the dynamic variations of the signals. Besides, they modify their behavior according to the input signal. Therefore, they can detect shape variations in the ensemble and thus they can obtain a better signal estimation. This paper focuses on (i) Model Respiratory signal with second order Auto Regressive process. Then randomly generated noises have been mixed with respiratory signal and nullify these noises using various adaptive filter algorithms (ii) to remove motion artifacts and 50Hz Power line interference from sinusoidal 0.18Hz respiratory signal using various adaptive filter algorithms. At the end of this paper, a performance study has been done between these algorithms based on various step sizes. It has been found that there will be always tradeoff between step sizes and Mean square error.
The Analysis of Aluminium Cantilever Beam with Piezoelectric Material by Chan...IRJET Journal
The document describes a study analyzing an aluminum cantilever beam with piezoelectric material to control vibrations. A finite element model of the beam was created in ANSYS software. Modal analysis was performed to determine the beam's natural frequencies without and with piezoelectric patches placed at different positions along the length. The first three natural frequencies of the bare beam were around 8 Hz, 48 Hz, and 118 Hz. Piezoelectric patches were found to shift the beam's natural frequencies higher. The results aim to validate a method for active vibration control of structures.
Autotuning of pid controller for robot arm and magnet levitation planteSAT Journals
Abstract
One of the most essential work of the control engineer is tuning of controller. Majority of the controller used in industry are of the
PID type. An auto tuning is one of the method of controller tuning in which tuning of the parameters of controller is done
automatically and possibly, without any user interaction expect from initiating the operation. Present study emphasis on the relay
based auto tuning of PID controller. An auto-tuning method is implemented based on a relay experiment to determine the ultimate
gain and the ultimate period, with which the PID parameters are obtained using the Ziegler-Nichols tuning rules. An auto tuning
of robot arm model and magnet levitation model are carried out. Performance of relay based auto tuning on the basis of integral
square error is better than artificial neural network.
Keywords: Relay auto tuning, PID, FOPDT, SOPDT, Integral square error.
A Study of Ball Bearing’s Crack Using Acoustic Signal / Vibration Signal and ...INFOGAIN PUBLICATION
The field of fault diagnostic in rotating machinery is vast, including the diagnosis of items such as rotating shafts, rolling element bearings, couplings, gears and so on. Vibration analysis is the main condition monitoring technique for machinery maintenance. The different types of faults that are observed in these areas and the methods of their diagnosis are accordingly great, including vibration analysis, model-based techniques, and statistical analysis and artificial intelligence techniques. However, they have difficulties with certain applications whose behavior is non-stationary and transient nature.
SIGNAL PROCESSING TECHNIQUES USED FOR GEAR FAULT DIAGNOSISJungho Park
The slides are about signal processing techniques widely used for gear fault diagnosis (also the techniques could be used for other various rotating machine diagnosis such as bearing, rotor, motor, etc.). The techniques include wavelet transform, EMD (empirical mode decomposition), HHT (Hilbert-Huang transform), AR-MED filter, Spectral kurtosis, and cyclo-stationary analysis.
IRJET- A Simple Approach to Identify Power System Transmission Line Faults us...IRJET Journal
This paper presents a Probabilistic Neural Network (PNN) approach for identifying and classifying faults on power transmission lines. The PNN is trained on voltage waveform data simulated using Electromagnetic Transient Program (EMTP) software for different fault types and locations on a 150km transmission line. Only two sets of simulated data are used to train the PNN, requiring less computation than other methods that preprocess data. The trained PNN is able to accurately identify and classify fault types based on the voltage waveform, which helps ensure reliable power transmission by isolating only faulty lines or phases.
Aeolian vibrations of overhead transmission line bundled conductors during in...Power System Operation
Part B of this paper proposes a method for assessing
the performance of spacer-dampers on a quad-bundled
conductor using an existing system identification
algorithm and experimental modal data obtained from
Aeolian vibration measurements. To generate the
frequency response function (FRF) as a force input, a
shaker was used and attached at a certain distance via
a rigid link, and acceleration was measured at the free
span. To ensure that the data was not compromised, the
excitation technique used was first evaluated in different
configuration scenarios in part A of this paper. Three
different commercial spacer-dampers were used in this
investigation. One was placed at the mid-span and the
other two placed at different locations. The damping
performance was evaluated in terms of the main fatigue
indicator, i.e. the bending stress envelope of both clamp
edges at the spacer-damper and at the termination
clamp. A better performance configuration of bundled
conductors is the one that generates a bending stress
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
KMEM4212_Applied Vibration_Group Assignment_Report_CL 3Max Lee
KMEM4212 Applied Vibration (University of Malaya)
Co-operative Learning 3 (CL 3)
This report contains a clear methodology that may be helpful for those who wish to run the experiment by themselves.
Sharing with you (Mechanical Engineering students) who may be benefited from it.
Feel free to connect with me at maxermesilliam@gmail.com
Wigner-Ville Distribution: In Perspective of Fault DiagnosisJungho Park
This document discusses the Wigner-Ville distribution and its properties for time-frequency analysis and fault diagnosis. It contains the following key points:
1. The Wigner-Ville distribution is defined as the Fourier transform of the autocorrelation function and represents the signal's energy distribution over time and frequency.
2. Important properties of the Wigner-Ville distribution include that it is real-valued, and its marginals yield the instantaneous power and energy spectral density. However, it can take on negative values for some signals.
3. For multi-component signals, cross-terms arise in the distribution due to quadratic calculation, which can interfere with interpretation but are not inherently undesirable.
Analytical Modeling of Vibration Signals from a Planetary Gear in Normal and ...Jungho Park
This document summarizes analytical modeling of vibration signals from a planetary gear. It first introduces dynamic modeling of planetary gears from previous literature. It then describes modeling normal vibration signals by considering gear configurations and phase differences. Code is shown to simulate the signals. Modeling of faulty signals is also discussed by considering amplitude modulation and frequency modulation. The document concludes by noting the need to simulate developed faulty behaviors and validate with test data.
Acoustic emission condition monitoring an application for wind turbine fault ...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A Simple and Robust Algorithm for the Detection of QRS ComplexesIJRES Journal
The objective of this paper is to develop an easy, efficient and robust algorithm for the analysis of electrocardiogram signals. The technique used in this algorithm is based on the use of Moving Average Filters and Adaptive Thresholding for QRS complex detection. Several established ECG databases published on PhysioNet with sampling frequency ranging from 128Hz- 1KHz, were used for analyzing the technique. The accuracy of the algorithm is determined on the basis of two statistical parameters: sensitivity (SE) and Positive Predictivity (+P).
Development of Seakeeping Test and Data Processing Systemijceronline
This document describes the development of a seakeeping test and data processing system. The system includes two main procedures: wave generation and data processing. In wave generation, a linear filtering method is used to generate irregular waves that meet a target spectrum. In data processing, time domain and frequency domain methods are used to analyze experimental data on irregular waves, ship motions, and hull stresses. The system was tested using experiments on a ship model in irregular waves and showed accurate simulation and reliable data processing.
Bearing fault detection using acoustic emission signals analyzed by empirical...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
This document analyzes various wavelet transforms for edge detection in X-ray bone images. It begins with an introduction to edge detection and its importance in medical imaging. Classical derivative operators can detect edges but also extract false information and are sensitive to noise. Wavelet transforms offer multi-resolution analysis to detect edges at different scales. The document then provides background on wavelet theory and discrete wavelet transforms. It analyzes applying various orthogonal wavelets like Haar, Daubechies, and Coiflet to X-ray images and compares their performance in edge detection based on metrics like edge detection accuracy and computation time. Haar wavelets performed best at detecting edges with better quality in less time.
Generation of Quantum Photon Information Using Extremely Narrow Optical Tweez...University of Malaya (UM)
A system of microring resonator (MRR) is presented to generate extremely narrow optical tweezers. An add/drop filter system consisting of one centered ring and one smaller ring on the left side can be used to generate extremely narrow pulse of optical tweezers. Optical tweezers generated by the dark-Gaussian behavior propagate via the MRRs system, where the input Gaussian pulse controls the output signal at the drop port of the system. Here the output optical tweezers can be connected to a quantum signal processing system (receiver), where it can be used to generate high capacity quantum codes within series of MRR’s and an add/drop filter. Detection of the encoded signals known as quantum bits can be done by the receiver unit system. Generated entangled photon pair propagates via an optical communication link. Here, the result of optical tweezers with full width at half maximum (FWHM) of 0.3 nm, 0.8 nm and 1.6 nm, 1.3 nm are obtained at the through and drop ports of the system respectively. These results used to be transmitted through a quantum signal processor via an optical computer network communication link.
Performance Analysis of Acoustic Echo Cancellation TechniquesIJERA Editor
Mainly, the adaptive filters are implemented in time domain which works efficiently in most of the applications. But in many applications the impulse response becomes too large, which increases the complexity of the adaptive filter beyond a level where it can no longer be implemented efficiently in time domain. An example of where this can happen would be acoustic echo cancellation (AEC) applications. So, there exists an alternative solution i.e. to implement the filters in frequency domain. AEC has so many applications in wide variety of problems in industrial operations, manufacturing and consumer products. Here in this paper, a comparative analysis of different acoustic echo cancellation techniques i.e. Frequency domain adaptive filter (FDAF), Least mean square (LMS), Normalized least mean square (NLMS) &Sign error (SE) is presented. The results are compared with different values of step sizes and the performance of these techniques is measured in terms of Error rate loss enhancement (ERLE), Mean square error (MSE)& Peak signal to noise ratio (PSNR).
Vibration Analysis and Modelling of a Cantilever Beam Muhammad Usman
This report in cooperates the techniques, adopted for the evaluation of vibration analysis of a cantilever beam using both techniques i.e. theoretical as well as the practical ones. Computer based analysis of a beam were also performed using Solid Works and Mat Lab software. These techniques helped a lot in finding the natural frequencies and in making the vibrational characteristic behavior of a cantilever beam thus steel used as a material.
Review of Space-charge Measurement using Pulsed Electro- Acoustic Method: Adv...IJERA Editor
The pulsed electro acoustic (PEA) technique is the most widely used method to measure space charge
distributions in insulating materials. The PEA technique has undergone some advancement since the over
twenty years it was first implemented such as in its spatial resolution and sensitivity. In this article a review of
the technique was carried out and its advantages, limitations, progress and prospects were discussed.
Quaternion Based Omnidirectional Machine Condition Monitoring SystemCSCJournals
Thermal monitoring is useful for revealing some serious electrical problems in a factory that often go undetected until a serious breakdown occurs. In factories, there are various types of functioning machines to be monitored. When there is any malfunctioning of a machine, extra heat will be generated which can be picked up by thermal camera for image processing and identification purpose. In this paper, a new and effective omnidirectional machine condition monitoring system applying log-polar mapper, quaternion based thermal image correlator and max-product fuzzy neural network classifier is proposed for monitoring machine condition in an omnidirectional view. With this monitoring system, it is convenient to detect and monitor the conditions of (overheat or not) of more than one machines in an omnidirectional view captured by using a single thermal camera. Log-polar mapping technique is used to unwarp omnidirectional thermal image into panoramic form. Two classification characteristics namely: peak to sidelobe ratio (PSR) and real to complex ratio of the discrete quaternion correlation output (p-value) are applied in the proposed machine condition monitoring system. Large PSR and p-value observe in a good match among correlation of the input thermal image with a particular reference image, while small PSR and p-value observe in a bad/not match among correlation of the input thermal image with a particular reference image. Simulation results also show that the proposed system is an efficient omnidirectional machine monitoring system with accuracy more than 97%.
DESIGN REALIZATION AND PERFORMANCE EVALUATION OF AN ACOUSTIC ECHO CANCELLATIO...sipij
Nowadays, in the field of communications, AEC (acoustic echo cancellation) is truly essential with respect
to the quality of multimedia transmission. In this paper, we designed and developed an efficient AEC based
on adaptive filters to improve quality of service in telecommunications against the phenomena of acoustic
echo, which is indeed a problem in hands-free communications.The main advantage of the proposed algorithm is its capacity of tracking non-stationary signals such as acoustic echo. In this work the acoustic echo cancellation (AEC) is modeled using a digital signal
processing technique especially Simulink Blocksets. The algorithm’s code is generated in Matlab Simulink
programming environment. At simulation level, results of simulink implementation prove that module
behavior is realistic when it comes to cancellation of echo in hands free communication using adaptive algorithm.Results obtained with our algorithm in terms of ERLE criteria are confronted to IUT-T recommendation
G.168.
This document describes a study that introduces a Modified Error Data Normalized Step Size (MEDNSS) algorithm for an adaptive noise canceller. The MEDNSS algorithm uses a time-varying step size that depends on normalization of both the error and data vectors. The performance of the MEDNSS algorithm is analyzed through computer simulation and compared to the Error Data Normalized Step Size algorithm in stationary and non-stationary environments with different noise power levels. Simulation results show the MEDNSS algorithm significantly improves minimizing signal distortion, excess mean square error, and misadjustment factor compared to the EDNSS algorithm.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Study of Ball Bearing’s Crack Using Acoustic Signal / Vibration Signal and ...INFOGAIN PUBLICATION
The field of fault diagnostic in rotating machinery is vast, including the diagnosis of items such as rotating shafts, rolling element bearings, couplings, gears and so on. Vibration analysis is the main condition monitoring technique for machinery maintenance. The different types of faults that are observed in these areas and the methods of their diagnosis are accordingly great, including vibration analysis, model-based techniques, and statistical analysis and artificial intelligence techniques. However, they have difficulties with certain applications whose behavior is non-stationary and transient nature.
SIGNAL PROCESSING TECHNIQUES USED FOR GEAR FAULT DIAGNOSISJungho Park
The slides are about signal processing techniques widely used for gear fault diagnosis (also the techniques could be used for other various rotating machine diagnosis such as bearing, rotor, motor, etc.). The techniques include wavelet transform, EMD (empirical mode decomposition), HHT (Hilbert-Huang transform), AR-MED filter, Spectral kurtosis, and cyclo-stationary analysis.
IRJET- A Simple Approach to Identify Power System Transmission Line Faults us...IRJET Journal
This paper presents a Probabilistic Neural Network (PNN) approach for identifying and classifying faults on power transmission lines. The PNN is trained on voltage waveform data simulated using Electromagnetic Transient Program (EMTP) software for different fault types and locations on a 150km transmission line. Only two sets of simulated data are used to train the PNN, requiring less computation than other methods that preprocess data. The trained PNN is able to accurately identify and classify fault types based on the voltage waveform, which helps ensure reliable power transmission by isolating only faulty lines or phases.
Aeolian vibrations of overhead transmission line bundled conductors during in...Power System Operation
Part B of this paper proposes a method for assessing
the performance of spacer-dampers on a quad-bundled
conductor using an existing system identification
algorithm and experimental modal data obtained from
Aeolian vibration measurements. To generate the
frequency response function (FRF) as a force input, a
shaker was used and attached at a certain distance via
a rigid link, and acceleration was measured at the free
span. To ensure that the data was not compromised, the
excitation technique used was first evaluated in different
configuration scenarios in part A of this paper. Three
different commercial spacer-dampers were used in this
investigation. One was placed at the mid-span and the
other two placed at different locations. The damping
performance was evaluated in terms of the main fatigue
indicator, i.e. the bending stress envelope of both clamp
edges at the spacer-damper and at the termination
clamp. A better performance configuration of bundled
conductors is the one that generates a bending stress
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
KMEM4212_Applied Vibration_Group Assignment_Report_CL 3Max Lee
KMEM4212 Applied Vibration (University of Malaya)
Co-operative Learning 3 (CL 3)
This report contains a clear methodology that may be helpful for those who wish to run the experiment by themselves.
Sharing with you (Mechanical Engineering students) who may be benefited from it.
Feel free to connect with me at maxermesilliam@gmail.com
Wigner-Ville Distribution: In Perspective of Fault DiagnosisJungho Park
This document discusses the Wigner-Ville distribution and its properties for time-frequency analysis and fault diagnosis. It contains the following key points:
1. The Wigner-Ville distribution is defined as the Fourier transform of the autocorrelation function and represents the signal's energy distribution over time and frequency.
2. Important properties of the Wigner-Ville distribution include that it is real-valued, and its marginals yield the instantaneous power and energy spectral density. However, it can take on negative values for some signals.
3. For multi-component signals, cross-terms arise in the distribution due to quadratic calculation, which can interfere with interpretation but are not inherently undesirable.
Analytical Modeling of Vibration Signals from a Planetary Gear in Normal and ...Jungho Park
This document summarizes analytical modeling of vibration signals from a planetary gear. It first introduces dynamic modeling of planetary gears from previous literature. It then describes modeling normal vibration signals by considering gear configurations and phase differences. Code is shown to simulate the signals. Modeling of faulty signals is also discussed by considering amplitude modulation and frequency modulation. The document concludes by noting the need to simulate developed faulty behaviors and validate with test data.
Acoustic emission condition monitoring an application for wind turbine fault ...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A Simple and Robust Algorithm for the Detection of QRS ComplexesIJRES Journal
The objective of this paper is to develop an easy, efficient and robust algorithm for the analysis of electrocardiogram signals. The technique used in this algorithm is based on the use of Moving Average Filters and Adaptive Thresholding for QRS complex detection. Several established ECG databases published on PhysioNet with sampling frequency ranging from 128Hz- 1KHz, were used for analyzing the technique. The accuracy of the algorithm is determined on the basis of two statistical parameters: sensitivity (SE) and Positive Predictivity (+P).
Development of Seakeeping Test and Data Processing Systemijceronline
This document describes the development of a seakeeping test and data processing system. The system includes two main procedures: wave generation and data processing. In wave generation, a linear filtering method is used to generate irregular waves that meet a target spectrum. In data processing, time domain and frequency domain methods are used to analyze experimental data on irregular waves, ship motions, and hull stresses. The system was tested using experiments on a ship model in irregular waves and showed accurate simulation and reliable data processing.
Bearing fault detection using acoustic emission signals analyzed by empirical...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
This document analyzes various wavelet transforms for edge detection in X-ray bone images. It begins with an introduction to edge detection and its importance in medical imaging. Classical derivative operators can detect edges but also extract false information and are sensitive to noise. Wavelet transforms offer multi-resolution analysis to detect edges at different scales. The document then provides background on wavelet theory and discrete wavelet transforms. It analyzes applying various orthogonal wavelets like Haar, Daubechies, and Coiflet to X-ray images and compares their performance in edge detection based on metrics like edge detection accuracy and computation time. Haar wavelets performed best at detecting edges with better quality in less time.
Generation of Quantum Photon Information Using Extremely Narrow Optical Tweez...University of Malaya (UM)
A system of microring resonator (MRR) is presented to generate extremely narrow optical tweezers. An add/drop filter system consisting of one centered ring and one smaller ring on the left side can be used to generate extremely narrow pulse of optical tweezers. Optical tweezers generated by the dark-Gaussian behavior propagate via the MRRs system, where the input Gaussian pulse controls the output signal at the drop port of the system. Here the output optical tweezers can be connected to a quantum signal processing system (receiver), where it can be used to generate high capacity quantum codes within series of MRR’s and an add/drop filter. Detection of the encoded signals known as quantum bits can be done by the receiver unit system. Generated entangled photon pair propagates via an optical communication link. Here, the result of optical tweezers with full width at half maximum (FWHM) of 0.3 nm, 0.8 nm and 1.6 nm, 1.3 nm are obtained at the through and drop ports of the system respectively. These results used to be transmitted through a quantum signal processor via an optical computer network communication link.
Performance Analysis of Acoustic Echo Cancellation TechniquesIJERA Editor
Mainly, the adaptive filters are implemented in time domain which works efficiently in most of the applications. But in many applications the impulse response becomes too large, which increases the complexity of the adaptive filter beyond a level where it can no longer be implemented efficiently in time domain. An example of where this can happen would be acoustic echo cancellation (AEC) applications. So, there exists an alternative solution i.e. to implement the filters in frequency domain. AEC has so many applications in wide variety of problems in industrial operations, manufacturing and consumer products. Here in this paper, a comparative analysis of different acoustic echo cancellation techniques i.e. Frequency domain adaptive filter (FDAF), Least mean square (LMS), Normalized least mean square (NLMS) &Sign error (SE) is presented. The results are compared with different values of step sizes and the performance of these techniques is measured in terms of Error rate loss enhancement (ERLE), Mean square error (MSE)& Peak signal to noise ratio (PSNR).
Vibration Analysis and Modelling of a Cantilever Beam Muhammad Usman
This report in cooperates the techniques, adopted for the evaluation of vibration analysis of a cantilever beam using both techniques i.e. theoretical as well as the practical ones. Computer based analysis of a beam were also performed using Solid Works and Mat Lab software. These techniques helped a lot in finding the natural frequencies and in making the vibrational characteristic behavior of a cantilever beam thus steel used as a material.
Review of Space-charge Measurement using Pulsed Electro- Acoustic Method: Adv...IJERA Editor
The pulsed electro acoustic (PEA) technique is the most widely used method to measure space charge
distributions in insulating materials. The PEA technique has undergone some advancement since the over
twenty years it was first implemented such as in its spatial resolution and sensitivity. In this article a review of
the technique was carried out and its advantages, limitations, progress and prospects were discussed.
Quaternion Based Omnidirectional Machine Condition Monitoring SystemCSCJournals
Thermal monitoring is useful for revealing some serious electrical problems in a factory that often go undetected until a serious breakdown occurs. In factories, there are various types of functioning machines to be monitored. When there is any malfunctioning of a machine, extra heat will be generated which can be picked up by thermal camera for image processing and identification purpose. In this paper, a new and effective omnidirectional machine condition monitoring system applying log-polar mapper, quaternion based thermal image correlator and max-product fuzzy neural network classifier is proposed for monitoring machine condition in an omnidirectional view. With this monitoring system, it is convenient to detect and monitor the conditions of (overheat or not) of more than one machines in an omnidirectional view captured by using a single thermal camera. Log-polar mapping technique is used to unwarp omnidirectional thermal image into panoramic form. Two classification characteristics namely: peak to sidelobe ratio (PSR) and real to complex ratio of the discrete quaternion correlation output (p-value) are applied in the proposed machine condition monitoring system. Large PSR and p-value observe in a good match among correlation of the input thermal image with a particular reference image, while small PSR and p-value observe in a bad/not match among correlation of the input thermal image with a particular reference image. Simulation results also show that the proposed system is an efficient omnidirectional machine monitoring system with accuracy more than 97%.
DESIGN REALIZATION AND PERFORMANCE EVALUATION OF AN ACOUSTIC ECHO CANCELLATIO...sipij
Nowadays, in the field of communications, AEC (acoustic echo cancellation) is truly essential with respect
to the quality of multimedia transmission. In this paper, we designed and developed an efficient AEC based
on adaptive filters to improve quality of service in telecommunications against the phenomena of acoustic
echo, which is indeed a problem in hands-free communications.The main advantage of the proposed algorithm is its capacity of tracking non-stationary signals such as acoustic echo. In this work the acoustic echo cancellation (AEC) is modeled using a digital signal
processing technique especially Simulink Blocksets. The algorithm’s code is generated in Matlab Simulink
programming environment. At simulation level, results of simulink implementation prove that module
behavior is realistic when it comes to cancellation of echo in hands free communication using adaptive algorithm.Results obtained with our algorithm in terms of ERLE criteria are confronted to IUT-T recommendation
G.168.
This document describes a study that introduces a Modified Error Data Normalized Step Size (MEDNSS) algorithm for an adaptive noise canceller. The MEDNSS algorithm uses a time-varying step size that depends on normalization of both the error and data vectors. The performance of the MEDNSS algorithm is analyzed through computer simulation and compared to the Error Data Normalized Step Size algorithm in stationary and non-stationary environments with different noise power levels. Simulation results show the MEDNSS algorithm significantly improves minimizing signal distortion, excess mean square error, and misadjustment factor compared to the EDNSS algorithm.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Study of Ball Bearing’s Crack Using Acoustic Signal / Vibration Signal and ...INFOGAIN PUBLICATION
The field of fault diagnostic in rotating machinery is vast, including the diagnosis of items such as rotating shafts, rolling element bearings, couplings, gears and so on. Vibration analysis is the main condition monitoring technique for machinery maintenance. The different types of faults that are observed in these areas and the methods of their diagnosis are accordingly great, including vibration analysis, model-based techniques, and statistical analysis and artificial intelligence techniques. However, they have difficulties with certain applications whose behavior is non-stationary and transient nature.
A Study of Ball Bearing’s Crack Using Acoustic Signal / Vibration Signal and ...INFOGAIN PUBLICATION
The field of fault diagnostic in rotating machinery is vast, including the diagnosis of items such as rotating shafts, rolling element bearings, couplings, gears and so on. Vibration analysis is the main condition monitoring technique for machinery maintenance. The different types of faults that are observed in these areas and the methods of their diagnosis are accordingly great, including vibration analysis, model-based techniques, and statistical analysis and artificial intelligence techniques. However, they have difficulties with certain applications whose behavior is non-stationary and transient nature.
Numerical simulation of flow modeling in ducted axial fan using simpson’s 13r...iaemedu
This document presents a new tristate switching median filtering technique for digital image enhancement. The proposed filter combines two decision-based median filters with a switching scheme to better detect and remove salt and pepper noise while preserving image details. Simulation results on the Lena test image show that the proposed filter achieves better performance than conventional filters in terms of noise removal and edge preservation, especially at higher noise levels. The filter works by applying two different decision-based median filters to the noisy image and comparing their outputs to the original pixel value using a threshold. Pixels are classified and processed differently depending on how their values relate to the filter outputs and threshold. The filter is evaluated quantitatively using peak signal-to-noise ratio to demonstrate its
A new tristate switching median filtering technique for image enhancementiaemedu
This document presents a new tristate switching median filtering technique for digital image enhancement. The proposed filter combines two decision-based median filters with a switching scheme to better detect and remove salt and pepper noise while preserving image details. Simulation results on the Lena test image show that the proposed filter achieves better performance than conventional filters in terms of noise removal and edge preservation, especially at higher noise levels. The filter works by applying two different decision-based median filters to the noisy image and comparing their outputs to the original pixel value using a threshold to make a switching decision. This allows the filter to take advantage of both filtering techniques to more accurately classify pixels and reduce noise without degrading image features.
The document discusses the controversy around purchasing a dedicated HDTV antenna. While they are marketed as being needed to receive high definition broadcasts, in reality all an antenna does is receive radio frequencies, including those used for HDTV broadcasts. A regular TV antenna can receive both standard definition and HDTV broadcasts as long as it covers the VHF and UHF bands. There is no technical need to purchase a specialized "HDTV antenna" to receive HD channels over the air. The document questions the value and necessity of paying more for an antenna marketed specifically for HDTV rather than a regular TV antenna.
Noise analysis & qrs detection in ecg signalsHarshal Ladhe
The document discusses removing noise from ECG signals using adaptive filtering techniques. It focuses on using an LMS algorithm to remove powerline interference at 50 Hz from ECG signals. The LMS algorithm is tested with different filter tap lengths and step sizes to determine the optimal parameters for noise cancellation. Additional filtering using notch filters is also explored to remove harmonics and high frequency noise. The results show that the LMS algorithm effectively removes powerline interference from ECG signals.
This document discusses acoustic echo cancellation (AEC) systems using artificial neural network algorithms. It begins with background on AEC and issues with nonlinear echo paths. It then presents an AEC system using an artificial neural network combined with an adaptive filter model to address nonlinear environments. Simulation results on Matlab demonstrate that the proposed neural network approach combined with a Laguerre filter achieves lower error and higher echo return loss than adaptive filter-only methods, showing it effectively reduces echo signals in linear and nonlinear systems. The paper concludes the combined neural network-filter algorithm is a promising approach for acoustic echo cancellation.
ECG signal denoising using a novel approach of adaptive filters for real-time...IJECEIAES
Electrocardiogram (ECG) is considered as the main signal that can be used to diagnose different kinds of diseases related to human heart. During the recording process, it is usually contaminated with different kinds of noise which includes power-line interference, baseline wandering and muscle contraction. In order to clean the ECG signal, several noise removal techniques have been used such as adaptive filters, empirical mode decomposition, Hilbert-Huang transform, wavelet-based algorithm, discrete wavelet transforms, modulus maxima of wavelet transform, patch based method, and many more. Unfortunately, all the presented methods cannot be used for online processing since it takes long time to clean the ECG signal. The current research presents a unique method for ECG denoising using a novel approach of adaptive filters. The suggested method was tested by using a simulated signal using MATLAB software under different scenarios. Instead of using a reference signal for ECG signal denoising, the presented model uses a unite delay and the primary ECG signal itself. Least mean square (LMS), normalized least mean square (NLMS), and Leaky LMS were used as adaptation algorithms in this paper.
This document presents a study on using acoustic signal analysis to detect faults in bearings. The study develops an experimental setup to acquire acoustic signals from bearings under different conditions, including with and without defects. The acoustic signals are processed using techniques like fast Fourier transforms and wavelet transforms to extract information about faults. Signals are analyzed from bearings with no defects, misalignment, looseness, missing balls, and combinations of defects. Results show the acoustic signal energy at different frequencies for healthy and faulty bearings. This acoustic signal analysis technique can be used to detect bearing faults and failures.
Analysis of harmonics using wavelet techniqueIJECEIAES
This paper proposes a wavelet technique to analyze harmonics in power system signals. The algorithm uses Daubechies 20 wavelet and decomposes signals into different frequency sub-bands corresponding to harmonic components. Simulation results on test signals containing various harmonic distortions show the wavelet technique can identify the time and frequency of harmonic disturbances with errors less than 1.2%. The wavelet approach provides an alternative for harmonic analysis that overcomes limitations of conventional Fourier-based methods.
Noise Cancellation in ECG Signals using ComputationallyCSCJournals
Several signed LMS based adaptive filters, which are computationally superior having multiplier free weight update loops are proposed for noise cancellation in the ECG signal. The adaptive filters essentially minimizes the mean-squared error between a primary input, which is the noisy ECG, and a reference input, which is either noise that is correlated in some way with the noise in the primary input or a signal that is correlated only with ECG in the primary input. Different filter structures are presented to eliminate the diverse forms of noise: 60Hz power line interference, baseline wander, muscle noise and the motion artifact. Finally, we have applied these algorithms on real ECG signals obtained from the MIT-BIH data base and compared its performance with the conventional LMS algorithm. The results show that the performance of the signed regressor LMS algorithm is superior than conventional LMS algorithm, the performance of signed LMS and sign-sign LMS based realizations are comparable to that of the LMS based filtering techniques in terms of signal to noise ratio and computational complexity.
Comparison of signal smoothing techniques for use in embedded system for moni...Dalton Valadares
Paper about the comparison between some signal smoothing techniques for use in an embedded system responsible for monitoring the biofuels quality, specificaly the oxidative stability.
Multiple Sensors Soft-Failure Diagnosis Based on Kalman Filtersipij
Sensor is the necessary components of the engine control system. Therefore, more and more work must do for improving sensors reliability. Soft failures are small bias errors or drift errors that accumulate relatively slowly with time in the sensed values that it must be detected because of it can be very easy to be mistaken for the results of noise. Simultaneous multiple sensors failures are rare events and must be considered. In order to solve this problem, a revised multiple-failure-hypothesis based testing is investigated. This approach uses multiple Kalman filters, and each of Kalman filter is designed based on a specific hypothesis for detecting specific sensors fault, and then uses Weighted Sum of Squared Residual (WSSR) to deal with Kalman filter residuals, and residual signals are compared with threshold in order to make fault detection decisions. The simulation results show that the proposed method can be used to detect multiple sensors soft failures fast and accurately.
Revealing and evaluating the influence of filters position in cascaded filter...nooriasukmaningtyas
In this paper, a new optimization on windowing technique based on finite
impulse response (FIR) filters is proposed for revealing and evaluating the
Influence of filters position in cascaded filter tested on the ECG signal denoising. baseline wander (BLW), power line interference (PLI) and
electromyography (EMG) noises are gettingremoved. The performance of the
adopted method is evaluated on the PTB diagnostic database. Subsequently,
the comparisons are based on signal to noise ratio (SNR) improvement and
mean square error (MSE) minimization. Where the Rectangular, and Kaiser
windows have been used for the more potent performances. The disparity
average (DA) of SNR values is detected; in both Kaiser and Rectangular
windows are assessed by ±0.38046dB and ±0.70278dB respectively, while
the MSE values were constant. The excellent configuration or filters position
(H-B-L) of the filtration system is selected according to high measurements
of SNR and low MSE too, to de-noise the ECG signals. First of all, this
applied approach has led to 31.30 dB SNR improvement with MSE
minimization of 26. 43%. This means that there is a significant contribution
to improving the field of filtration.
Novel method to find the parameter for noise removal from multi channel ecg w...eSAT Journals
This document presents a novel method for removing noise from multi-channel electrocardiogram (ECG) waveforms using a multi-swarm optimization (MSO) approach. The method involves extracting features from ECG data, using MSO to identify an optimal cutoff frequency parameter for a finite impulse response (FIR) filter, and applying the FIR filter using the identified parameter to remove noise from the ECG signals. The MSO approach divides particles into multiple swarms that each focus on a region of the search space, helping to overcome sensitivity to initial positions found in traditional particle swarm optimization. The resulting filtered ECG signals are evaluated against original clean signals to validate the noise removal performance of the MSO-identified cutoff frequency parameter and
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Assessment of Gearbox Fault DetectionUsing Vibration Signal Analysis and Acou...IOSR Journals
Maintenance is a set of organised activities that are carried out in order to keep an item in its best
operational condition with minimum cost acquired. Predictive maintenance (PdM) is one of the maintenance
program that recommends maintenance decisions based on the information collected through condition
monitoring techniques, statistical process control or equipment performance for the purpose of early detection
and elimination of equipment defects that could lead to unplanned downtime of machinery or unnecessary
expenditures. Particularly Gears and rolling element bearings are critical elements in rotating machinery, so
predictive maintenance is often applied to them. Fault signals of gearboxes or rolling-element bearings are nonstationary.
This paper concludes with a brief discussion on current practices of PDM methodologies such as
vibration analysis and Acoustic Emission analysis, which are widely used as they offers a complimentary tool
for health monitoring or assessment of gears in rotating machineries
Similar to Rolling Element Bearing Condition Monitoring using Filtered Acoustic Emission (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
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IEEE Slovenia GRSS
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The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
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The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
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Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
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The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
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[13]. In literature it can be found the application of LMS algorithm in adaptive filters as the de-noising tool to
cancel the noise [14], [15].
In this work initially the ANC is implemented on acquired acoustic signal. For ANC the de-noised
principle of noise removal is followed. In the de-noising process three adaptive algorithms are tested and
their performance is compared in terms of their SNR (signal to noise ratio) and MSE (mean square error).
The LMS, wavelet and EDM algorithm is used for the same. As EMD algorithm is found as the best then the
filtered acoustic signal from the EDM de-noising is used for the further processing and analysis. The analysis
to detect the fault is done in time, frequency and Time-frequency domain. For this experiment one healthy
bearing and three defective bearing is used.
1.1 Active noise cancellation
Active noise cancellation is used for the pre processing of the acoustic signal. Acoustic signals are
always contaminated with noises and other external interferences, which must be removed or filtered before
further processing and analysis.
1.1.1. LMS de-noising
The least mean square (LMS) algorithm is used as a de-noising tool for the ANC. It is used to update
the adaptive filter coefficients as:
( 1) ( ) ( 1) ( )l lw n w n x n e n (1)
The ANC steps using the Least Mean Square algorithm is explained briefly as follows [11]:
a) The selection of the step size and the filter length L is made.
b) The output of the adaptive filter is calculated as 1
0( ) ( ) ( 1)L
lly n w n x n
c) The error signal is calculated as ( ) ( ) ( )e n d n y n
d) The coefficients of the adaptive filter is updated by using the following formula :
( 1) ( ) ( ) ( )l lw n w n x n l e n ,
where 0,1....., 1l L .
The selection of the step size value is very important as it affects the convergence speed. The proper selection
of filter length is also very important [12].
1.1.2. Empirical mode decomposition
This is another adaptive algorithm used for noise cancellation. This is suitable when the noise is
non-stationary in nature. This algorithm is based on empirical basis functions. The original signal ( ) is
decomposed into the set { } where represent intrinsic mode
functions (IMF) and are residual terms.
( ) ∑ ( ) ( ) (2)
The Empirical mode decomposition is an adaptive method to recognize oscillations from the
signal ( ). Like discrete wavelet transfer, EMD method decompose a signal into so-called intrinsic mode
functions(IMF).
1.1.3. Wavelet de-noising
This technique is one of the most popular and efficient techniques for the filtering of the noise. It
starts with the decomposition of the signal into successive approximation and details. Wavelet de-noising
performs correlation analysis. The expected value of the output turn out to be maximum if the input noisy
signal looks a lot like the picked mother wavelet function. As the wavelet transform is linear it works best for
the additive noise.
1.2. Time domain study
In time domain analysis the statistical features are computed from the vibration signature. By
comparing these statistical features the faults in the system can be identified. The statistical parameters used
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for the time domain analysis are RMS, skewness, mean, peak value, crest factor, kurtosis, standard deviation,
clearance factor, impulse factor and shape factor.
1.3. Frequency domain study
In time domain analysis some information may not be revealed so frequency domain can is used to
reveal those information which is not possible in time domain. The signal in time domain is basically
converted to frequency domain by employing the Fourier transform. In this analysis the acoustic signal peak
is displayed in the frequency spectrum and provide the information in frequency domain. The characteristic
fault frequencies can be calculated by the following equations:
( ) ( ) (3)
( ) ( ) (4)
1.4. Time-frequency study
In some cases time or frequency analysis alone may not give adequate information about the fault in
the rotating machine. So time-frequency analysis is needed for this purpose to give better analysis of the
fault. Wavelet transform is used for this purpose. The signals are processed by the wavelet transform to
generate the two dimensional map of WT coefficients to get the required time frequency information. It
provides the information simultaneously both in time and scale. In time-frequency methods of fault detection
the contour plots are visually observed. The fault can be detected by visually monitoring the changes that
occurred in the features of the distribution in the contour plots.
2. PERFORMANCE COMPARISON OF ANC FILTERING TECHNIQUES
In this section the performance of the ANC filtering techniques used to remove the noise is
compared. Comparison is made on the basis of SNR (signal to noise ratio) and MSE (mean square error). In
the experimental setup one defective bearing is placed. Then the acoustic signal is acquired. Then the three
ANC techniques are implemented on these acquired acoustic signals. The acquired noisy acoustic signal and
the filtered signal after ANC is shown in Figure 1. The comparison parameters of the three de-noising ANC
techniques are tabulated in Table 1.
Figure 1. Comparison of ANC techniques
Table 1. Parameters Comparison for ANC Techniques
ANC Techniques SNR MSE
LMS 11.068 0.0283
EMD 14.863 0.0210
Wavelet 13.061 0.0264
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From this comparison EMD is found better. So for the pre-processing stage the EMD active noise
cancellation is selected and implemented on all the acoustic signals acquired from the experimental set-up
and then the filtered acoustic signals are used for the further processing and analysis. The filtered acoustic
signals are shown in Figure 5.
3. EXPERIMENT
To implement the proposed method of diagnose the defect present in the bearing an experimental
set-up is made. The experimental set-up and its model is shown in Figure 2 and Figure 3 respectively. It
consists of a single phase induction motor of 0.5hp and its speed is 1400 RPM with no load at 230V and
50Hz supply.
Figure 2. Hardware set-up for the experiment Figure 3. Model of set-up for the experiment
The acoustic data is acquired using a Brüel & Kjær 2 channel handheld Sound analyser. The
bearings under test are mounted on the shaft of the motor. For this work one healthy bearing, two outer race
fault bearing is used. The healthy bearing, outer race fault type-1 and outer race fault type-2 bearing is shown
in Figure 4, Figure 5 and Figure 6 respectively.
Figure 4. The healthy bearing Figure 5. The type-1 defect bearing
Figure 6. The type-2 bearing
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Initially the healthy bearing is mounted on the shaft and the corresponding acoustic signal is
acquired. Similarly the three defective bearings are mounted one by one and the corresponding acoustic
signal is acquired. All the acquired acoustic signals after filtration is processed for further analysis. The
filtered acoustic signals are shown in Figure 7, Figure 8 and Figure 9.
Figure 7. Filtered acoustic signal of
healthy bearing (B1)
Figure 8. Filtered acoustic signal of outer race fault
type-1 defect bearing (B2)
Figure 9. Filtered acoustic signal of outer race fault type-2 defect bearing (B3)
4. RESULTS AND DISCUSSION
This section shows the static (time), the frequency and the time-frequency (wavelet) analysis of the
filtered acoustic signal to diagnose the fault in the bearings.
4.1. Time domain (static) analysis
Static parameters like, skewness, shape factor, kurtosis, RMS, crest factor, peak value etc. is
calculated from the filtered acoustic signatures by using the mathematical formulas and is tabulated in
Table 2. From the Table 2 the variations of the parameters for the faulty bearings as compared to healthy
bearings are well observed. Also the difference in variations for different type of fault is well observed.
Table 2. Time Domain Parameter Comparison
Sl. No Static Parameters Healthy Bearing Type-I
Defect Bearing
Type-II
Defect Bearing
1 Root mean square (RMS) 0.0134 0.0261 0.0203
2 Mean 0.4554 1.3406 0.9112
3 Peak value 0.0773 0.1734 0.1432
4 Crest factor 5.7737 6.6469 7.0564
5 Skewness -0.0052 -0.2564 0.1228
6 Kurtosis 3.8532 9.0461 5.7542
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Sl. No Static Parameters Healthy Bearing Type-I
Defect Bearing
Type-II
Defect Bearing
7 Variance 0.0143 0.1104 0.035
8 Standard deviation 0.0134 0.0261 0.0203
9 Clearance factor 716.0322 665.9750 624.7892
10 Impulse factor 7.4381 10.7450 9.4580
11 Shape factor 1.2883 1.6165 7.4381
4.2. Frequency domain (FFT) analysis
Fast fourier transform (FFT) is used for this analysis. The FFT of healthy bearing and outer race fault
type-1 defect bearing is compared and is shown in Figure 10. Similarly FFT comparison of the healthy
bearing and the outer race fault type-2 defect bearing is compared and is shown in Figure 11. Then the
bearing characteristic frequency (BCF) also known as the outer race defect frequency (ORDF) is calculated
using the geometric configuration of the bearing. Then the comparison between healthy and defective bearing
at ORDF is shown in Figure 12 and Figure 13.
Figure 10. Comparison of FFT of healthy bearing (B1)
and type-1 defect bearing (B2)
Figure 11. Comparison of FFT of healthy bearing
(B1) and type-2 defect bearing (B3)
Figure 12. FFT comparison of healthy (B1) and
type-1 defect bearing (B2) at BCF
Figure 13. FFT comparison of healthy (B1) and
type-2 defect bearing (B3) at BCF
4.3. Time-frequency analysis
Morlet wavelet is used for this purpose as the mother wavelet. 2D scalograms based on morlet
wavelet are plotted for the acoustic signals. A difference is clearly visible around scale value of equivalent
BCF for the healthy, type-1 defect and type-2 defect bearing as shown in Figure 14, Figure 15 and Figure 16
respectively.
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Figure 14. Scalogram for acoustic signature of
healthy bearing (B1)
Figure 15. Scalogram for acoustic signature of outer
race fault type-1 defect bearing (B2)
Figure 16 Scalogram for acoustic signature of outer race fault type-2 defect bearing (B3)
5. CONCLUSION
The present experimental work shows the application of active noise cancellation filtering technique
to improve the signal to noise ratio of the acoustic signature before its use in analyzing the defects. The ANC
filtering used in this work at the pre-processing stage is adaptive in nature and gives better performance in
improving the SNR of the measured signal. The performance of the three different ANC techniques are
compared and the best one is used at the pre-processing stage to remove the noise. In this work though static
analysis gives the information about the defect present in the bearing but frequency analysis gives the
comparison in a better way and the time-frequency analysis is more informative about the defect as it gives
the information both in time and frequency scale. The scalograms from the wavelet analysis gives precise
information of the defect present in the bearing. This work shows that the acoustic emission is a good
alternative which can be used to diagnose the defect in the bearing of rolling element.
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BIOGRAPHIES OF AUTHORS
Sudarsan Sahoo was born on July 1980 in Cuttack, India. He received his Bachelor degree from
SLIET, Punjab, India in Instrumentation Engineering and Master degree from Indian Institute of
Science, Bangalore, India in Instrumentation engineering. He is currently Assistant Professor at the
Department of Electronics and Instrumentation Engineering, National Institute of Technology
Silchar (NIT Silchar), India. His research is specialized in acoustic and biomedical signal processing,
intelligent instrumentation and industrial noise control.
Jitendra Kumar Das was born in 1970 in cuttack, India. He received both his Master degree and
PhD degree from National Institute of Technology Rourkela (NIT Rourkela),India. He is currently
working as Professor at School of Electronics Engineering, KIIT University, India. His research is
specialized in VLSI, Signal processing.