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.
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.
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.
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.
Rolling Element Bearing Condition Monitoring using Filtered Acoustic Emission IJECEIAES
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.
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.
Review on vibration analysis with digital image processingIAEME Publication
This document summarizes research on analyzing vibrations using digital image processing. It discusses using cameras to capture images of vibrating plates and analyzing the images using algorithms to determine resonant frequencies and mode shapes. MATLAB is used for the image processing and analysis. The document reviews several related studies on using techniques like electronic speckle pattern interferometry to characterize vibrations of plates and structures non-contact. It also presents a case study on using digital image processing to characterize the vibration of a tuning fork by measuring its amplitude response over different excitation frequencies.
IRJET - Essential Features Extraction from Aaroh and Avroh of Indian Clas...IRJET Journal
The document summarizes research on extracting essential features from the aaroh and avroh of the Indian classical raga Yaman. It discusses extracting various time domain, spectral, cepstral and other features using audio signal processing and machine learning techniques. Time domain features like zero crossing rate and autocorrelation are extracted. Spectral features including the spectrogram, mel-spectrogram, spectral centroid, roll-off and others are analyzed. Cepstral features such as MFCC are also discussed. The goal is to extract relevant features that can be used for audio classification, recognition and other tasks related to Indian classical music.
TFM is an advanced ultrasonic NDT method that uses phased array probes to focus ultrasonic signals at many points within an area of interest, creating a matrix effect. Each element in the phased array probe pulses individually and receives signals from all other elements to reconstruct the signal data. This allows TFM to focus ultrasonic signals at over 65,000 points, providing more detailed information than traditional phased array testing that focuses at only one depth. Examples shown include using TFM to detect cracks, corrosion, and other defects in various industrial components and materials.
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.
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.
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.
Rolling Element Bearing Condition Monitoring using Filtered Acoustic Emission IJECEIAES
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.
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.
Review on vibration analysis with digital image processingIAEME Publication
This document summarizes research on analyzing vibrations using digital image processing. It discusses using cameras to capture images of vibrating plates and analyzing the images using algorithms to determine resonant frequencies and mode shapes. MATLAB is used for the image processing and analysis. The document reviews several related studies on using techniques like electronic speckle pattern interferometry to characterize vibrations of plates and structures non-contact. It also presents a case study on using digital image processing to characterize the vibration of a tuning fork by measuring its amplitude response over different excitation frequencies.
IRJET - Essential Features Extraction from Aaroh and Avroh of Indian Clas...IRJET Journal
The document summarizes research on extracting essential features from the aaroh and avroh of the Indian classical raga Yaman. It discusses extracting various time domain, spectral, cepstral and other features using audio signal processing and machine learning techniques. Time domain features like zero crossing rate and autocorrelation are extracted. Spectral features including the spectrogram, mel-spectrogram, spectral centroid, roll-off and others are analyzed. Cepstral features such as MFCC are also discussed. The goal is to extract relevant features that can be used for audio classification, recognition and other tasks related to Indian classical music.
TFM is an advanced ultrasonic NDT method that uses phased array probes to focus ultrasonic signals at many points within an area of interest, creating a matrix effect. Each element in the phased array probe pulses individually and receives signals from all other elements to reconstruct the signal data. This allows TFM to focus ultrasonic signals at over 65,000 points, providing more detailed information than traditional phased array testing that focuses at only one depth. Examples shown include using TFM to detect cracks, corrosion, and other defects in various industrial components and materials.
IRJET- Comparative Analysis of Different Modulation Technique for Free-Space ...IRJET Journal
This document compares different modulation techniques for free-space optical communication, including On-Off Keying (OOK), Binary Phase-Shift Keying (BPSK), Differential Phase-Shift Keying (DPSK) and Pulse Position Modulation (PPM). It analyzes their characteristics such as bit error rate, signal-to-noise ratio, and receiver sensitivity. Through simulations and theoretical analysis, it finds that PPM has the highest power efficiency of the techniques considered. OOK has moderate signal-to-noise ratio requirements but also low cost, while BPSK can enable longer-distance communication with coherent detection.
IRJET- Speech Signal Processing for Classification of Parkinson’s DiseaseIRJET Journal
This document discusses a study that aimed to analyze voice samples from people with Parkinson's disease and healthy individuals to classify them using machine learning algorithms. It extracted features like jitter, shimmer, noise harmonic ratio, and harmonic noise ratio from voice recordings. It then used support vector machines and K-nearest neighbors algorithms to classify the samples, achieving 91.35% accuracy with SVM and 90.67% accuracy with KNN. The study demonstrated that voice analysis is a potential method for Parkinson's disease diagnosis and classification.
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.
Survey Paper for Different Video Stabilization TechniquesIRJET Journal
This document summarizes and compares three video stabilization techniques: Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), and block-based methods. SIFT extracts distinctive keypoints from videos that are invariant to scale and rotation, but it is computationally slow. SURF is faster than SIFT and also extracts robust features. Block-based methods partition video frames into macroblocks and estimate motion between frames by block matching, using metrics like mean absolute difference. It has lower complexity than SIFT and SURF but provides good stability for video stabilization. The document analyzes the performance of these techniques and their application in video stabilization.
Development of a Condition Monitoring Algorithm for Industrial Robots based o...IJECEIAES
Signal processing plays a significant role in building any condition monitoring system. Many types of signals can be used for condition monitoring of machines, such as vibration signals, as in this research; and processing these signals in an appropriate way is crucial in extracting the most salient features related to different fault types. A number of signal processing techniques can fulfil this purpose, and the nature of the captured signal is a significant factor in the selection of the appropriate technique. This chapter starts with a discussion of the proposed robot condition monitoring algorithm. Then, a consideration of the signal processing techniques which can be applied in condition monitoring is carried out to identify their advantages and disadvantages, from which the time-domain and discrete wavelet transform signal analysis are selected.
Signal Processing and Soft Computing Techniques for Single and Multiple Power...idescitation
This paper reviews various techniques used for detection and classification of power quality events. It divides the techniques into those for single events versus combined events. For single events, techniques like wavelet transforms, statistical estimators, and intelligent methods are discussed. For combined events, papers addressing harmonic disturbances combined with others are summarized. The paper also includes a table providing a comparative analysis of several references based on aspects like classification accuracy, noise tolerance, and computation time. It concludes that the field is growing and future work could address techniques for large data and detection of both single and combined disturbances simultaneously.
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.
Comparative Analysis of Natural Frequency of Transverse Vibration of a Cantil...IRJET Journal
This document presents a comparative analysis of the natural frequency of transverse vibration of a cantilever beam using analytical and experimental methods. Analytical calculations are performed to determine the natural frequencies of the first three modes of vibration of the cantilever beam. Experimental testing is conducted using an impact hammer, accelerometer, and FFT analyzer. The natural frequencies measured experimentally are found to be close to those calculated analytically. The results demonstrate that analytical and experimental methods can both accurately determine the natural frequencies of a cantilever beam's vibration.
1) The document discusses using discrete wavelet transforms to analyze vibration signals from roller bearings to detect faults. It proposes a new feature - summing the squared wavelet decomposition coefficients at each level - and compares it to the traditional energy-based feature.
2) An experiment is described where vibration signals are collected from a test rig under normal conditions and with introduced inner race, outer race, and combined faults. The signals are decomposed using discrete wavelet transforms.
3) Features are then extracted from the wavelet decompositions using both the proposed summed squared coefficient feature and the traditional energy-based feature. A decision tree is used to classify the features and determine which feature performs better at detecting the faults.
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is an open access international journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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
Characterization of transients and fault diagnosis in transformer by discreteIAEME Publication
This document discusses using discrete wavelet transform (DWT) and artificial neural networks (ANN) to characterize transients and diagnose faults in transformers. It begins with an introduction to the problem and background on using the second harmonic component for discrimination. It then discusses why time-frequency information is needed and the advantages of wavelet transforms over Fourier transforms. The document describes collecting data from a test transformer under normal and faulted conditions. It explains using DWT for feature extraction and visualizing the wavelet decomposition levels to characterize magnetizing inrush versus inter-turn faults. Finally, it proposes using ANN trained on the wavelet spectral energies for automated discrimination between fault cases.
IRJET- A Review for Reduction of Noise by Wavelet Transform in Audio SignalsIRJET Journal
This document reviews noise reduction techniques for audio signals using wavelet transforms. It discusses how wavelet transforms can be used to decompose audio signals into frequency subbands, with noise typically concentrated in high frequencies and signal in low frequencies. The document outlines the history of wavelet transforms and different types of noise including white, colored, impulsive, and transient noise. It then reviews several studies on using wavelet transforms like biorthogonal and Daubechies wavelets to denoise audio signals, finding wavelet thresholding and adaptive filters can effectively remove noise and improve metrics like signal-to-noise ratio. Comparing techniques found Daubechies wavelets generally perform best at audio noise reduction.
Conditioning Monitoring of Gearbox Using Different Methods: A ReviewIJMER
Gears are important element in a variety of industrial applications such as machine tool
and gearboxes. An unexpected failure of the gear may cause significant economic losses. For that
reason, fault diagnosis in gears has been the subject of intensive research. Vibration signal analysis
has been widely used in the fault detection of rotation machinery. Fault diagnosis plays an important
role in condition monitoring to enhance the machine time. In view of this, the present investigation
focused on the development of Fault diagnosis system of gearboxes based on the vibration signatures
and Artificial Neural Networks. In the present investigation to generate the vibration signatures an
experimental set-up has been fabricated with sensing and measuring equipment. The prominent faults,
wear, crack, broken tooth and insufficient lubrication of the gear were practically induced in the
present investigation. Vibration signatures of the gearbox were collected by transmitting the motion at
constant speed with gears having no fault, without applying any load. By inducing one fault at a time,
vibration signatures were collected with different degrees of wear on a gear tooth, a gear with a
broken tooth, tooth with crack and with insufficient lubrication. As the vibration data of maximum
amplitudes was found to be inseparable, fault diagnosis based on this data was not possible. Five
prominent statistical features were extracted based on data pertaining to maximum amplitudes of
vibration and used fault diagnosis. Due overlapping of this data, it was decided to use ANN based
fault diagnosis system for the present investigation. The set of statistical features were extracted based
on data pertaining to maximum amplitudes of vibration and used them as input parameters to the
ANN based fault diagnosis system designed.
Condition Monitoring of Rotating Equipment Considering the Cause and Effects ...IJMERJOURNAL
This document summarizes research on condition monitoring of rotating equipment using vibration analysis. It discusses various signal processing techniques used for fault detection and diagnosis, including time-domain analysis, frequency domain analysis, time-frequency analysis using wavelet transforms, and support vector machines. It also reviews literature on prognostics approaches that use condition data to predict failures through artificial intelligence techniques. The document aims to provide an overview of recent developments in diagnostic and prognostic models, algorithms, and technologies for processing sensor data from condition monitoring systems.
A Review on Speech Enhancement System using FIR FilterIRJET Journal
This document reviews a speech enhancement system using an FIR filter. It discusses how speech signals become distorted by noise and the need to recover the original clean speech. The proposed system uses an adaptive FIR filter with adjustable coefficients to remove noise from noisy speech signals. The FIR filter acts as a low-pass filter, removing high frequency noise. The adaptive algorithm adjusts the filter coefficients according to the Lyapunov stability theory to minimize error between the input and output signals, effectively removing noise. The system can enhance both linear and non-linear signals and offers improved performance over traditional noise reduction methods.
This document discusses using artificial neural networks (ANN) and Daubechies wavelet transforms to diagnose faults in induction motor bearings based on vibration signal analysis. It presents the following key points:
1) Vibration signals were collected from a test rig under healthy and faulty bearing conditions. Statistical features were extracted from the signals using different Daubechies wavelet transforms.
2) These statistical features were used as input for an ANN to classify the bearing conditions. The Db4 wavelet produced the most accurate fault classifications by the ANN.
3) The methodology involved feature extraction from raw vibration signals using Daubechies wavelets, selecting the best wavelet based on classification accuracy, and using
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
A Survey on Classification of Power Quality Disturbances in a Power SystemIJERA Editor
This document provides a survey of techniques for classifying power quality disturbances in a power system. It discusses various power quality issues and types of disturbances such as transients, interruptions, sags, swells, waveform distortions, and frequency variations. It then describes several signal processing techniques used for feature extraction, including Fourier transform, short-time Fourier transform, S-transform, Hilbert-Huang transform, Kalman filter, and wavelet transform. Finally, it reviews various classification methods such as artificial neural networks, fuzzy expert systems, adaptive neuro-fuzzy systems, genetic algorithms, and support vector machines that have been applied to classify power quality disturbances.
A review of Noise Suppression Technology for Real-Time Speech EnhancementIRJET Journal
This document summarizes research on noise suppression technology for real-time speech enhancement. It discusses how noise suppression has gained interest due to advances in deep learning techniques. It describes how noise suppression works by using multiple microphones to capture audio signals, which are then processed using algorithms to separate and suppress background noises while enhancing speech. Deep learning has achieved promising results for noise suppression by training models to detect human voice between different input noises. The document also reviews conventional uses of noise suppression in devices and limitations, and how using deep learning allows for more effective separation of noise from sound signals.
This document discusses the application of the Fourier transform in signal processing. It was submitted by five second-year undergraduate students from Asansol Engineering College under the guidance of Dr. Arnab Bandyopadhyay. The document provides an introduction to Fourier transforms and their use in representing signals as weighted sums of sinusoids. It also discusses previous work, the Fourier transform calculation, applications in areas like automotive and medicine, and draws conclusions about reducing noise and enabling modulation/demodulation of signals through Fourier analysis.
IRJET- Comparative Analysis of Different Modulation Technique for Free-Space ...IRJET Journal
This document compares different modulation techniques for free-space optical communication, including On-Off Keying (OOK), Binary Phase-Shift Keying (BPSK), Differential Phase-Shift Keying (DPSK) and Pulse Position Modulation (PPM). It analyzes their characteristics such as bit error rate, signal-to-noise ratio, and receiver sensitivity. Through simulations and theoretical analysis, it finds that PPM has the highest power efficiency of the techniques considered. OOK has moderate signal-to-noise ratio requirements but also low cost, while BPSK can enable longer-distance communication with coherent detection.
IRJET- Speech Signal Processing for Classification of Parkinson’s DiseaseIRJET Journal
This document discusses a study that aimed to analyze voice samples from people with Parkinson's disease and healthy individuals to classify them using machine learning algorithms. It extracted features like jitter, shimmer, noise harmonic ratio, and harmonic noise ratio from voice recordings. It then used support vector machines and K-nearest neighbors algorithms to classify the samples, achieving 91.35% accuracy with SVM and 90.67% accuracy with KNN. The study demonstrated that voice analysis is a potential method for Parkinson's disease diagnosis and classification.
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.
Survey Paper for Different Video Stabilization TechniquesIRJET Journal
This document summarizes and compares three video stabilization techniques: Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), and block-based methods. SIFT extracts distinctive keypoints from videos that are invariant to scale and rotation, but it is computationally slow. SURF is faster than SIFT and also extracts robust features. Block-based methods partition video frames into macroblocks and estimate motion between frames by block matching, using metrics like mean absolute difference. It has lower complexity than SIFT and SURF but provides good stability for video stabilization. The document analyzes the performance of these techniques and their application in video stabilization.
Development of a Condition Monitoring Algorithm for Industrial Robots based o...IJECEIAES
Signal processing plays a significant role in building any condition monitoring system. Many types of signals can be used for condition monitoring of machines, such as vibration signals, as in this research; and processing these signals in an appropriate way is crucial in extracting the most salient features related to different fault types. A number of signal processing techniques can fulfil this purpose, and the nature of the captured signal is a significant factor in the selection of the appropriate technique. This chapter starts with a discussion of the proposed robot condition monitoring algorithm. Then, a consideration of the signal processing techniques which can be applied in condition monitoring is carried out to identify their advantages and disadvantages, from which the time-domain and discrete wavelet transform signal analysis are selected.
Signal Processing and Soft Computing Techniques for Single and Multiple Power...idescitation
This paper reviews various techniques used for detection and classification of power quality events. It divides the techniques into those for single events versus combined events. For single events, techniques like wavelet transforms, statistical estimators, and intelligent methods are discussed. For combined events, papers addressing harmonic disturbances combined with others are summarized. The paper also includes a table providing a comparative analysis of several references based on aspects like classification accuracy, noise tolerance, and computation time. It concludes that the field is growing and future work could address techniques for large data and detection of both single and combined disturbances simultaneously.
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.
Comparative Analysis of Natural Frequency of Transverse Vibration of a Cantil...IRJET Journal
This document presents a comparative analysis of the natural frequency of transverse vibration of a cantilever beam using analytical and experimental methods. Analytical calculations are performed to determine the natural frequencies of the first three modes of vibration of the cantilever beam. Experimental testing is conducted using an impact hammer, accelerometer, and FFT analyzer. The natural frequencies measured experimentally are found to be close to those calculated analytically. The results demonstrate that analytical and experimental methods can both accurately determine the natural frequencies of a cantilever beam's vibration.
1) The document discusses using discrete wavelet transforms to analyze vibration signals from roller bearings to detect faults. It proposes a new feature - summing the squared wavelet decomposition coefficients at each level - and compares it to the traditional energy-based feature.
2) An experiment is described where vibration signals are collected from a test rig under normal conditions and with introduced inner race, outer race, and combined faults. The signals are decomposed using discrete wavelet transforms.
3) Features are then extracted from the wavelet decompositions using both the proposed summed squared coefficient feature and the traditional energy-based feature. A decision tree is used to classify the features and determine which feature performs better at detecting the faults.
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is an open access international journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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
Characterization of transients and fault diagnosis in transformer by discreteIAEME Publication
This document discusses using discrete wavelet transform (DWT) and artificial neural networks (ANN) to characterize transients and diagnose faults in transformers. It begins with an introduction to the problem and background on using the second harmonic component for discrimination. It then discusses why time-frequency information is needed and the advantages of wavelet transforms over Fourier transforms. The document describes collecting data from a test transformer under normal and faulted conditions. It explains using DWT for feature extraction and visualizing the wavelet decomposition levels to characterize magnetizing inrush versus inter-turn faults. Finally, it proposes using ANN trained on the wavelet spectral energies for automated discrimination between fault cases.
IRJET- A Review for Reduction of Noise by Wavelet Transform in Audio SignalsIRJET Journal
This document reviews noise reduction techniques for audio signals using wavelet transforms. It discusses how wavelet transforms can be used to decompose audio signals into frequency subbands, with noise typically concentrated in high frequencies and signal in low frequencies. The document outlines the history of wavelet transforms and different types of noise including white, colored, impulsive, and transient noise. It then reviews several studies on using wavelet transforms like biorthogonal and Daubechies wavelets to denoise audio signals, finding wavelet thresholding and adaptive filters can effectively remove noise and improve metrics like signal-to-noise ratio. Comparing techniques found Daubechies wavelets generally perform best at audio noise reduction.
Conditioning Monitoring of Gearbox Using Different Methods: A ReviewIJMER
Gears are important element in a variety of industrial applications such as machine tool
and gearboxes. An unexpected failure of the gear may cause significant economic losses. For that
reason, fault diagnosis in gears has been the subject of intensive research. Vibration signal analysis
has been widely used in the fault detection of rotation machinery. Fault diagnosis plays an important
role in condition monitoring to enhance the machine time. In view of this, the present investigation
focused on the development of Fault diagnosis system of gearboxes based on the vibration signatures
and Artificial Neural Networks. In the present investigation to generate the vibration signatures an
experimental set-up has been fabricated with sensing and measuring equipment. The prominent faults,
wear, crack, broken tooth and insufficient lubrication of the gear were practically induced in the
present investigation. Vibration signatures of the gearbox were collected by transmitting the motion at
constant speed with gears having no fault, without applying any load. By inducing one fault at a time,
vibration signatures were collected with different degrees of wear on a gear tooth, a gear with a
broken tooth, tooth with crack and with insufficient lubrication. As the vibration data of maximum
amplitudes was found to be inseparable, fault diagnosis based on this data was not possible. Five
prominent statistical features were extracted based on data pertaining to maximum amplitudes of
vibration and used fault diagnosis. Due overlapping of this data, it was decided to use ANN based
fault diagnosis system for the present investigation. The set of statistical features were extracted based
on data pertaining to maximum amplitudes of vibration and used them as input parameters to the
ANN based fault diagnosis system designed.
Condition Monitoring of Rotating Equipment Considering the Cause and Effects ...IJMERJOURNAL
This document summarizes research on condition monitoring of rotating equipment using vibration analysis. It discusses various signal processing techniques used for fault detection and diagnosis, including time-domain analysis, frequency domain analysis, time-frequency analysis using wavelet transforms, and support vector machines. It also reviews literature on prognostics approaches that use condition data to predict failures through artificial intelligence techniques. The document aims to provide an overview of recent developments in diagnostic and prognostic models, algorithms, and technologies for processing sensor data from condition monitoring systems.
A Review on Speech Enhancement System using FIR FilterIRJET Journal
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A Study of Ball Bearing’s Crack Using Acoustic Signal / Vibration Signal and Analysis of Fast Fourier Transformation Spectrum
1. International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-2, Issue-5, May- 2016]
Infogain Publication (Infogainpublication.com) ISSN: 2454-1311
www.ijaems.com Page | 449
A Study of Ball Bearing’s Crack Using Acoustic
Signal / Vibration Signal and Analysis of Fast
Fourier Transformation Spectrum
Himanshu1
, Rahul2
1
Department of ME, CBS Group of Institution, Jhajjar, Haryana, India
2
Assistant Professor, Department of ME, CBS Group of Institution, Jhajjar, Haryana, India
Abstract— 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.
Keywords— Acoustic signal, FFT, Frequency, Signal
Processing.
I. INTRODUCTION
Vibration Analysis, applied in an industrial or
maintenance environment aims to reduce maintenance
costs and equipment downtime by detecting equipment
faults. Vibration analysis is a key component of a
Condition Monitoring program, and is often referred to as
Predictive Maintenance. In many cases, however,
vibration is undesirable, wasting energy and creating
unwanted sound. For example, the vibrational motions
of engines, electric motors, or any mechanical device in
operation are typically unwanted. Such vibrations could
be caused by imbalances in the rotating parts,
uneven friction, or the meshing of gear teeth. Careful
designs usually minimize unwanted vibrations. The
studies of sound and vibration are closely related. Sound,
or pressure waves, are generated by vibrating structures,
these pressure waves can also induce the vibration of
structures. Hence, attempts to reduce noise are often
related to issues of vibration.
Using vibration analysis on rotating machinery enables
the early detection of faults before breakdown. This will
reduce economical losses to production and equipment,
saving industry millions of dollars in machine down time.
The evaluations of the changes in vibration response,
critical speeds and stability of a machine have become an
important part of most maintenance predictive programs.
This will enable the condition monitoring and diagnostic
of a machine; therefore repairs can be planned and
performed economically. Vibration signal analysis has
been extensively used in the fault detection and condition
monitoring of rotating machinery. Many schemes
predictive maintenance and machinery diagnostic systems
use the condition machine to identify and classify faults
through the analysis of vibration signals.
II. EXPERIMENTAL WORK AND
OBJECTIVES
Bearing plays a role in supporting the rotating shaft.
Defects in bearing may lead to decrease in transmission
efficiency, jerk and noise. We have implemented method
based on acoustics and vibrations data to identify the
effect of misalignment of shaft in bearing arrangement.
The experiments are done on the bearing in a shaft under
loading conditions. The outer shell of ball bearing is
covered by housing. In the present study, a system model
capable of describing the theoretical dynamic behavior
resulting from wear of inner race is developed during
continuous working. Depending on the characteristics of
the raw acoustic signal obtained from experiment,
conventional filters based on Fourier transform is applied.
The Fourier transform expands the original function
(signal) in terms of ortho-normal basis function of sine
and cosine waves of infinite duration; however the
wavelet transform can do it for finite duration as well.
One of the great advantages of the wavelet filtering is that
the time information is not lost. And the problem
undertaken has practical importance in operation, on-line
inspection, failure prediction and maintenance of rotating.
A comparison between experimental and numerical
results clearly indicates that validity of the theoretical
model was successfully verified for wear at inner race.
The results show that the fault mechanical looseness and
the effect of the evolution of wear can be monitored and
detected during the machine run-up without passing by
critical speed. Extensive numerical and experimental
2. International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-2, Issue-5, May- 2016]
Infogain Publication (Infogainpublication.com) ISSN: 2454-1311
www.ijaems.com Page | 450
results show the ability and feasibility of the application
of wavelet analysis in the diagnostic of faults inserted in
the experimental set-up is very suitable to non-stationary
signal analysis. Finally, results show that the sensivity
and efficiency in the fault diagnostic using transient
response during run-up is higher than steady state
response for rotating machinery.
In this project work we tried to analyze the effect of wear
of inner race on the bearing on the spectrum of acoustic
signal and developed a method to identify such defect.
The main objective of this project work can be used in
predictive maintenance to proceed further we have to
know certain things which is basically the signal
processing.
III. SIGNAL
Communication happens in the form of signals. Signals
are transmission of energy (mechanical, electrical or
light) through appropriate media. A signal that is constant
and changed once conveys single information.
Amplitude, frequency and phase are the attributes of a
signal that changes with time.
1. Amplitude (A), strength of the signal over time,
volts, current.
2. Frequency (f) is the rate at which the signal repeats.
Hertz.
An equivalent parameter is period (T), is the
amount of time for one repetition, (T = 1/f).
3. Phase (φ) is a measure of the relative position in time
within a single period of a signal.
IV. VIBRATIONAL SIGNAL
Every moving body contains some vibrations in that and
in our project we are dealing with wear in the bearing.
When the bearing are rotated with faulty condition then
there is change in vibrational signal in comparison to the
ideal case. This change in the vibrational signal is shown
in the form of spectrum, which is useful in our analysis
purpose because in our project the acoustic signal and
vibrational signal occurs with each other. If the magnitude
of the acoustic signal is more then it means the system
has more vibrations. Here are some common causes of
vibration.
V. SIGNAL PROCESSING
New technologies and applications in various fields are
now poised to take advantage of signal processing
algorithms. Let us understand the concept of signal
processing with a short example, as we know the signals
are carrier of information, both useful and unwanted.
Therefore extracting or enhancing the useful information
from a mix of conflicting information is a simplest form
of signal processing. More generally signal processing is
an operation for extracting, enhancing, storing and
transmitting useful information. Two types of signal
processors are there in general which are given below.
5.1 Analog signal processor (ASP): these are used to
process the analog signals. And analog signals are those
signals, which vary continuously in time and amplitude.
Example radio and television receivers.
5.2 Digital signal processor (DSP): These are used to
process the digital signals. The digital signal, which is in
the form of bits and can be represented by the binary
numbers.
5.3 Signal analysis:
This task deals with the measurement of signal properties.
It is generally a frequency- domain operation. Some of its
applications are
1. Spectrum (frequency and / or phase analysis)
analysis
2. Speech recognition
3. Speaker verification
4. Target detection
VI. FAST FOURIER TRANSFORMATION
(FFT)
The Fast Fourier Transform (FFT) is a powerful tool for
analyzing and measuring signals. For example, you can
effectively acquire time-domain signals, measure the
frequency content, and convert the results to real-world
units and displays as shown on traditional spectrum. By
using FFT you can build a lower cost measurement
system and avoid the communication overhead of
working with a stand-alone instrument. Plus, you have the
flexibility of configuring your measurement processing to
meet your needs.
F(s) = ∫ f (t) exp -i ω t dt
F(s) is a Fourier transform of f (t)
VII. METHODOLOGY
7.1 Design the system for acquisition of acoustic &
vibration signal. A system has to be developed to record
the audio signal in the frequency range of 20 Hz to 20
KHz. Mike will act as sensor and will be interfaced with
the computer. The acoustic signal will be recorded and
stored in the computer for the different conditions.
Specific arrangements are done over the system to make it
compatible for recording the vibration data signals.
Vibration data signals are recorded over the digital
storage oscilloscope with the aid of accelerometer.
7.2 Processing of the acquired acoustic signal. The
acoustic signal will be processed in the MATLAB
environment in order to improve the signal-to-noise ratio.
Low pass filters (up to 500Hz), FFT spectrum and
Decomposition scalogram is used for this.
3. International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-2, Issue-5, May- 2016]
Infogain Publication (Infogainpublication.com) ISSN: 2454-1311
www.ijaems.com Page | 450
results show the ability and feasibility of the application
of wavelet analysis in the diagnostic of faults inserted in
the experimental set-up is very suitable to non-stationary
signal analysis. Finally, results show that the sensivity
and efficiency in the fault diagnostic using transient
response during run-up is higher than steady state
response for rotating machinery.
In this project work we tried to analyze the effect of wear
of inner race on the bearing on the spectrum of acoustic
signal and developed a method to identify such defect.
The main objective of this project work can be used in
predictive maintenance to proceed further we have to
know certain things which is basically the signal
processing.
III. SIGNAL
Communication happens in the form of signals. Signals
are transmission of energy (mechanical, electrical or
light) through appropriate media. A signal that is constant
and changed once conveys single information.
Amplitude, frequency and phase are the attributes of a
signal that changes with time.
1. Amplitude (A), strength of the signal over time,
volts, current.
2. Frequency (f) is the rate at which the signal repeats.
Hertz.
An equivalent parameter is period (T), is the
amount of time for one repetition, (T = 1/f).
3. Phase (φ) is a measure of the relative position in time
within a single period of a signal.
IV. VIBRATIONAL SIGNAL
Every moving body contains some vibrations in that and
in our project we are dealing with wear in the bearing.
When the bearing are rotated with faulty condition then
there is change in vibrational signal in comparison to the
ideal case. This change in the vibrational signal is shown
in the form of spectrum, which is useful in our analysis
purpose because in our project the acoustic signal and
vibrational signal occurs with each other. If the magnitude
of the acoustic signal is more then it means the system
has more vibrations. Here are some common causes of
vibration.
V. SIGNAL PROCESSING
New technologies and applications in various fields are
now poised to take advantage of signal processing
algorithms. Let us understand the concept of signal
processing with a short example, as we know the signals
are carrier of information, both useful and unwanted.
Therefore extracting or enhancing the useful information
from a mix of conflicting information is a simplest form
of signal processing. More generally signal processing is
an operation for extracting, enhancing, storing and
transmitting useful information. Two types of signal
processors are there in general which are given below.
5.1 Analog signal processor (ASP): these are used to
process the analog signals. And analog signals are those
signals, which vary continuously in time and amplitude.
Example radio and television receivers.
5.2 Digital signal processor (DSP): These are used to
process the digital signals. The digital signal, which is in
the form of bits and can be represented by the binary
numbers.
5.3 Signal analysis:
This task deals with the measurement of signal properties.
It is generally a frequency- domain operation. Some of its
applications are
1. Spectrum (frequency and / or phase analysis)
analysis
2. Speech recognition
3. Speaker verification
4. Target detection
VI. FAST FOURIER TRANSFORMATION
(FFT)
The Fast Fourier Transform (FFT) is a powerful tool for
analyzing and measuring signals. For example, you can
effectively acquire time-domain signals, measure the
frequency content, and convert the results to real-world
units and displays as shown on traditional spectrum. By
using FFT you can build a lower cost measurement
system and avoid the communication overhead of
working with a stand-alone instrument. Plus, you have the
flexibility of configuring your measurement processing to
meet your needs.
F(s) = ∫ f (t) exp -i ω t dt
F(s) is a Fourier transform of f (t)
VII. METHODOLOGY
7.1 Design the system for acquisition of acoustic &
vibration signal. A system has to be developed to record
the audio signal in the frequency range of 20 Hz to 20
KHz. Mike will act as sensor and will be interfaced with
the computer. The acoustic signal will be recorded and
stored in the computer for the different conditions.
Specific arrangements are done over the system to make it
compatible for recording the vibration data signals.
Vibration data signals are recorded over the digital
storage oscilloscope with the aid of accelerometer.
7.2 Processing of the acquired acoustic signal. The
acoustic signal will be processed in the MATLAB
environment in order to improve the signal-to-noise ratio.
Low pass filters (up to 500Hz), FFT spectrum and
Decomposition scalogram is used for this.
4. International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-2, Issue-5, May- 2016]
Infogain Publication (Infogainpublication.com) ISSN: 2454-1311
www.ijaems.com Page | 452
Fig.8.6:Bearing with wear of 2mm by width and thickness
at outer race with corresponding FFT Spectrum
IX. CONCLUSION AND FUTURE SCOPE
Results can also be summarized in the form of FFT graph
values which are same as of earlier shown in the chapter 3
but here just an expansion for the particular value is done.
The significance of these plots is that we can obtain the
rotational speed at any desired point and also at any
instant.
These are basically FFT plots for the minimum speeds of
all the four signals. Here also the value of the first peak
and last peak is given in the graph itself, through which
the value of the speed may be calculated.
As the speed over which frequency peaks is obtained is
1460 r.p.m. which can be converted in frequency as:
1460/60 = 24.333 r.p.s
And it defines the frequency as:
24.333*7 = 208 Hz (as number of balls are 7)
9.1 FUTURE SCOPE
This process can be used for live analysis of other
machines, example internal combustion engines,
Compressors, Turbines etc. we will use our this topic of
vibration and acoustics for fault diagnosis in the higher
studies as here a very vast scope is available.
9.2 APPLICATIONS
Applications of the method prescribe above includes:
Condition monitoring of equipment It includes
Railway tracks
Machine having rotary components e.g lathe
Fault diagnosis of rotary components like
impeller, pumps etc
Critical equipment where access is not feasible
like control rod evaluation mechanism in nuclear
power plant
Also used for fault diagnosis of hard disk of
computer
Quality inspection of biscuits (like crispness
testing)
REFRENCES
[1] [ A. Choudhury, 1999 ], “A review of vibration and
acoustic measurement methods for the detection of
defects in rolling element bearings”, Tribology
International 32 (1999) 469–480
[2] [ Chun-Kai Hung, 2004 ], “Constructing a wavelet-
based envelope function for vibration signal
analysis”, Mechanical Systems and Signal
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[3] [ Fengcai Wang, 2009 ], “Multi-frequency
identification method in signal processing”, Digital
Signal Processing 19 (2009) 555–566
[4] [ G.K. Singh, 2009 ], “Isolation and identification
of dry bearing faults in induction machine using
wavelet transform”, Tribology International 42
(2009) 849–861
[5] [ Hai Qiu et al. , 2006 ], “Wavelet filter-based weak
signature detection method and its application on
rolling element bearing prognostics”, Journal of
Sound and Vibration, vol 289, pp 1066-109
[6] [ Jian-Da Wu. , 2006 ], “Continuous wavelet
transform technique for fault signal diagnosis of
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international, vol 39,pp 304-311
[7] [ Krzysztof , 2001 ], “Some aspects of acoustic
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[8] [ Miroslav et al., 2003 ], “Rotating machines parts
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[9] [ Massimo et al. , 2005], “Fast, robust and efficient
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[10] MATLAB User Guide, The Math Works, Inc.
(1999).