This document summarizes research on fault detection of face milling cutters using signal processing techniques. Vibration signals were collected from a milling machine under healthy and faulty conditions (flank wear, chipping, breakage). The signals were analyzed using time-domain, frequency-domain (spectrum), cepstrum, and continuous wavelet transform plots. Experimental results showed that the 54th multiple of the tooth passing frequency (810 Hz) dominated in the cepstrum and wavelet plots, indicating it could be used for fault recognition. The continuous wavelet transform technique provided frequency components over time and was thus concluded to be useful for recognizing faults in face milling tools.
Signal Processing and Soft Computing Techniques for Single and Multiple Power...idescitation
In this paper review of various methods and
approaches that are used for the detection and classification
of power quality (PQ) events are presented. Survey has been
divided into two main categories one in which only single
events are considered and another in which combined events
are considered. Table has been also designed to present the
comparative analysis of some references. Application of
wavelet, need of power quality indices and optimization
techniques has been also described in the paper. The aim of
this paper is to show the Performance of various methodologies
so that appropriate technique would be used for the detection
and classification of PQ events.
Fault Diagnostics of Rolling Bearing based on Improve Time and Frequency Doma...ijsrd.com
The neural network based approaches a feed forward neural network trained with Back Propagation technique was used for automatic diagnosis of defects in bearings. Vibration time domain signals were collected from a normal bearing and defective bearings under various speed conditions. The signals were processed to obtain various statistical parameters, which are good indicators of bearing condition, then best features are selected from graphical method and these inputs were used to train the neural network and the output represented the bearing states. The trained neural networks were used for the recognition of bearing states. The results showed that the trained neural networks were able to distinguish a normal bearing from defective bearings with 83.33 % reliability. Moreover, the network was able to classify the bearings into different states with success rates better than those achieved with the best among the state-of-the-art techniques.
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.
Signal Processing and Soft Computing Techniques for Single and Multiple Power...idescitation
In this paper review of various methods and
approaches that are used for the detection and classification
of power quality (PQ) events are presented. Survey has been
divided into two main categories one in which only single
events are considered and another in which combined events
are considered. Table has been also designed to present the
comparative analysis of some references. Application of
wavelet, need of power quality indices and optimization
techniques has been also described in the paper. The aim of
this paper is to show the Performance of various methodologies
so that appropriate technique would be used for the detection
and classification of PQ events.
Fault Diagnostics of Rolling Bearing based on Improve Time and Frequency Doma...ijsrd.com
The neural network based approaches a feed forward neural network trained with Back Propagation technique was used for automatic diagnosis of defects in bearings. Vibration time domain signals were collected from a normal bearing and defective bearings under various speed conditions. The signals were processed to obtain various statistical parameters, which are good indicators of bearing condition, then best features are selected from graphical method and these inputs were used to train the neural network and the output represented the bearing states. The trained neural networks were used for the recognition of bearing states. The results showed that the trained neural networks were able to distinguish a normal bearing from defective bearings with 83.33 % reliability. Moreover, the network was able to classify the bearings into different states with success rates better than those achieved with the best among the state-of-the-art techniques.
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.
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
Correlation Analysis of Tool Wear and Cutting Sound SignalIJRES Journal
With the classic signal analysis and processing method, the cutting of the audio signal in time
domain and frequency domain analysis. We reached the following conclusions: in the time domain analysis,
cutting audio signals mean and the variance associated with tool wear state change occurred did not change
significantly, and tool wear is not high degree of correlation, and the mean-square value of the audio signal
changes in the size and tool wear the state has a good relationship.
Investigation of various orthogonal wavelets for precise analysis of X-ray im...IJERA Editor
Now-a-days X-rays are playing very important role in medicine. One of the most important applications of Xray
is detecting fractures in bones. X-ray provides important information about the type and location of the
fracture. Sometimes it is not possible to detect the fractures in X-rays with naked eye. So it needs further
processing to detect the fractures even at minute levels. To detect minute fractures, in this paper various edge
feature extraction methods are analyzed which helps medical practitioners to study the bone structure, detects
the bone fracture, measurement of fracture treatment, and treatment planning prior to surgery. The classical
derivative edge detection operators such as Roberts, Prewitt, sobel, Laplacian of Gaussian can be used as edge
detectors, but a lot of false edge information will be extracted. Therefore a technique based on orthogonal
wavelet transforms like Haar, daubechies, coiflet, symlets are applied to detect the edges and are compared.
Among all the methods, Haar wavelet transform method performs well in detecting the edges with better
quality. The various performance metrics like Ratio of Edge pixels to size of image (REPS), peak signal to noise
ratio (PSNR) and computation time are compared for various wavelets.
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.
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.
diagnosis of faults in bearings is very crucial for the reliable. This paper focuses on fault diagnosis of induction motor
bearing having localized defects using Daubechies wavelets-based feature extraction. In present study Machinery Fault
Simulator (MFS) test rig used for fault diagnosis of NSK-6203 deep groove ball bearing. Vibration signals collected from
the various bearing conditions- healthy bearing (HB), outer race defect (ORD), inner race defect (IRD), ball defect (BD)
and combined bearing defect (CBD). The extraction of statistical features carried out using various Daubechies wavelet
coefficients from raw vibration signals. Lastly, the bearing faults are classified using these statistical features as input to
Artificial Neural Network (ANN) technique used for faults classifications. The test result shows that ANN identifies the
fault categories of rolling element bearing more accurately for Db4 and has a better diagnosis performance as compared to
other Daubechies wavelets with ANN classifier.
Rolling Element Bearing Condition Monitoring using Filtered Acoustic Emission IJECEIAES
The defect present in the bearing of a rolling element may affect the performance of the rotating machinery and may reduce its efficiency. For this reason the condition monitoring of a rolling element bearing is very essential. So many measuring parameters are there to diagnose the fault in a rolling element bearing. Acoustic signature monitoring is one of them. Every rolling element bearing has its own acoustic signature when it is in healthy condition and when the bearing get defected then there is a change in its original acoustic signature. This change in acoustic signature can be monitored and analyzed to detect the fault present in the bearing. But the noise present in the acquired acoustic signal may affect the analysis. So the noisy acoustic signal must be filtered before the analysis. In this work the experiment is performed in two stages. In first stage the filtration of the acquired acoustic signal is done by employing the active noise cancellation (ANC) filtering techniques. In second stage the filtered signal is used for the further analysis. For the analysis initially the static analysis is done and then the frequency and the time-frequency analysis is done to diagnose the defect in the bearing. From all the three analysis the information about the defect present in the bearing is well detected.
Condition Monitoring of Rotating Equipment Considering the Cause and Effects ...IJMERJOURNAL
ABSTRACT: This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing Condition Monitoring with emphasis on models, algorithms and technologies for data processing and maintenance decision-making. Realising the increasing trend of using multiple sensors in condition monitoring, the authors also discuss different techniques for multiple sensor data fusion. The paper concludes with a brief discussion on current practices, possible future trends of Condition Monitoring with a brief outline on the novelty of the current research work.
An Accurate Classification Method of Harmonic Signals in Power Distribution S...TELKOMNIKA JOURNAL
This paper presents an accurate classification method of harmonic signal in power distribution
system by using S-transform (ST). ST has a capability of representing signals in jointly time-frequency
domain and known as time frequency representation (TFR). The spectral parameters are estimated from
TFR in order to identify the characteristics and to classify the harmonic signals. The classification of
harmonic signals with the utilization of pattern recognition approach which is rule-based classifier of 100
unique signals is according to the IEEE standard 519:2014. The accuracy of the proposed method is
determined by using MAPE and the results proved that the method provides high accuracy of harmonic
signal classification. Additionally, S-transform also gives 100 percent correct classification of harmonic
signals. It is proven that the proposed method is accurate in detecting and classifying harmonic signals in
the distribution system.
An Improved Detection and Classification Technique of Harmonic Signals in Pow...Yayah Zakaria
This paper introduces an improved detection and classification technique of harmonic signals in power distribution using time-frequency distribution (TFD) analysis which is spectrogram. The spectrogram is an appropriate approach to signify signals in jointly time-frequency domain and known as time frequency representation (TFR). The spectral information of signals can
be observed and estimated plainly from TFR due to identify the
characteristics of the signals. Based on rule-based classifier and the threshold settings that referred to IEEE Standard 1159 2009, the detection and classification of harmonic signals for 100 unique signals consist of various characteristic of harmonics are carried out successfully. The accuracy of proposed method is examined by using MAPE and the result show that the technique provides high accuracy. In addition, spectrogram also gives 100 percent correct classification of harmonic signals. It is proven that the proposed method is accurate, fast and cost efficient for detecting and classifying harmonic signals in distribution system.
An Improved Detection and Classification Technique of Harmonic Signals in Pow...IJECEIAES
This paper introduces an improved detection and classification technique of harmonic signals in power distribution using time-frequency distribution (TFD) analysis which is spectrogram. The spectrogram is an appropriate approach to signify signals in jointly time-frequency domain and known as time frequency representation (TFR). The spectral information of signals can be observed and estimated plainly from TFR due to identify the characteristics of the signals. Based on rule-based classifier and the threshold settings that referred to IEEE Standard 1159 2009, the detection and classification of harmonic signals for 100 unique signals consist of various characteristic of harmonics are carried out successfully. The accuracy of proposed method is examined by using MAPE and the result show that the technique provides high accuracy. In addition, spectrogram also gives 100 percent correct classification of harmonic signals. It is proven that the proposed method is accurate, fast and cost efficient for detecting and classifying harmonic signals in distribution system.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
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
Correlation Analysis of Tool Wear and Cutting Sound SignalIJRES Journal
With the classic signal analysis and processing method, the cutting of the audio signal in time
domain and frequency domain analysis. We reached the following conclusions: in the time domain analysis,
cutting audio signals mean and the variance associated with tool wear state change occurred did not change
significantly, and tool wear is not high degree of correlation, and the mean-square value of the audio signal
changes in the size and tool wear the state has a good relationship.
Investigation of various orthogonal wavelets for precise analysis of X-ray im...IJERA Editor
Now-a-days X-rays are playing very important role in medicine. One of the most important applications of Xray
is detecting fractures in bones. X-ray provides important information about the type and location of the
fracture. Sometimes it is not possible to detect the fractures in X-rays with naked eye. So it needs further
processing to detect the fractures even at minute levels. To detect minute fractures, in this paper various edge
feature extraction methods are analyzed which helps medical practitioners to study the bone structure, detects
the bone fracture, measurement of fracture treatment, and treatment planning prior to surgery. The classical
derivative edge detection operators such as Roberts, Prewitt, sobel, Laplacian of Gaussian can be used as edge
detectors, but a lot of false edge information will be extracted. Therefore a technique based on orthogonal
wavelet transforms like Haar, daubechies, coiflet, symlets are applied to detect the edges and are compared.
Among all the methods, Haar wavelet transform method performs well in detecting the edges with better
quality. The various performance metrics like Ratio of Edge pixels to size of image (REPS), peak signal to noise
ratio (PSNR) and computation time are compared for various wavelets.
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.
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.
diagnosis of faults in bearings is very crucial for the reliable. This paper focuses on fault diagnosis of induction motor
bearing having localized defects using Daubechies wavelets-based feature extraction. In present study Machinery Fault
Simulator (MFS) test rig used for fault diagnosis of NSK-6203 deep groove ball bearing. Vibration signals collected from
the various bearing conditions- healthy bearing (HB), outer race defect (ORD), inner race defect (IRD), ball defect (BD)
and combined bearing defect (CBD). The extraction of statistical features carried out using various Daubechies wavelet
coefficients from raw vibration signals. Lastly, the bearing faults are classified using these statistical features as input to
Artificial Neural Network (ANN) technique used for faults classifications. The test result shows that ANN identifies the
fault categories of rolling element bearing more accurately for Db4 and has a better diagnosis performance as compared to
other Daubechies wavelets with ANN classifier.
Rolling Element Bearing Condition Monitoring using Filtered Acoustic Emission IJECEIAES
The defect present in the bearing of a rolling element may affect the performance of the rotating machinery and may reduce its efficiency. For this reason the condition monitoring of a rolling element bearing is very essential. So many measuring parameters are there to diagnose the fault in a rolling element bearing. Acoustic signature monitoring is one of them. Every rolling element bearing has its own acoustic signature when it is in healthy condition and when the bearing get defected then there is a change in its original acoustic signature. This change in acoustic signature can be monitored and analyzed to detect the fault present in the bearing. But the noise present in the acquired acoustic signal may affect the analysis. So the noisy acoustic signal must be filtered before the analysis. In this work the experiment is performed in two stages. In first stage the filtration of the acquired acoustic signal is done by employing the active noise cancellation (ANC) filtering techniques. In second stage the filtered signal is used for the further analysis. For the analysis initially the static analysis is done and then the frequency and the time-frequency analysis is done to diagnose the defect in the bearing. From all the three analysis the information about the defect present in the bearing is well detected.
Condition Monitoring of Rotating Equipment Considering the Cause and Effects ...IJMERJOURNAL
ABSTRACT: This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing Condition Monitoring with emphasis on models, algorithms and technologies for data processing and maintenance decision-making. Realising the increasing trend of using multiple sensors in condition monitoring, the authors also discuss different techniques for multiple sensor data fusion. The paper concludes with a brief discussion on current practices, possible future trends of Condition Monitoring with a brief outline on the novelty of the current research work.
An Accurate Classification Method of Harmonic Signals in Power Distribution S...TELKOMNIKA JOURNAL
This paper presents an accurate classification method of harmonic signal in power distribution
system by using S-transform (ST). ST has a capability of representing signals in jointly time-frequency
domain and known as time frequency representation (TFR). The spectral parameters are estimated from
TFR in order to identify the characteristics and to classify the harmonic signals. The classification of
harmonic signals with the utilization of pattern recognition approach which is rule-based classifier of 100
unique signals is according to the IEEE standard 519:2014. The accuracy of the proposed method is
determined by using MAPE and the results proved that the method provides high accuracy of harmonic
signal classification. Additionally, S-transform also gives 100 percent correct classification of harmonic
signals. It is proven that the proposed method is accurate in detecting and classifying harmonic signals in
the distribution system.
An Improved Detection and Classification Technique of Harmonic Signals in Pow...Yayah Zakaria
This paper introduces an improved detection and classification technique of harmonic signals in power distribution using time-frequency distribution (TFD) analysis which is spectrogram. The spectrogram is an appropriate approach to signify signals in jointly time-frequency domain and known as time frequency representation (TFR). The spectral information of signals can
be observed and estimated plainly from TFR due to identify the
characteristics of the signals. Based on rule-based classifier and the threshold settings that referred to IEEE Standard 1159 2009, the detection and classification of harmonic signals for 100 unique signals consist of various characteristic of harmonics are carried out successfully. The accuracy of proposed method is examined by using MAPE and the result show that the technique provides high accuracy. In addition, spectrogram also gives 100 percent correct classification of harmonic signals. It is proven that the proposed method is accurate, fast and cost efficient for detecting and classifying harmonic signals in distribution system.
An Improved Detection and Classification Technique of Harmonic Signals in Pow...IJECEIAES
This paper introduces an improved detection and classification technique of harmonic signals in power distribution using time-frequency distribution (TFD) analysis which is spectrogram. The spectrogram is an appropriate approach to signify signals in jointly time-frequency domain and known as time frequency representation (TFR). The spectral information of signals can be observed and estimated plainly from TFR due to identify the characteristics of the signals. Based on rule-based classifier and the threshold settings that referred to IEEE Standard 1159 2009, the detection and classification of harmonic signals for 100 unique signals consist of various characteristic of harmonics are carried out successfully. The accuracy of proposed method is examined by using MAPE and the result show that the technique provides high accuracy. In addition, spectrogram also gives 100 percent correct classification of harmonic signals. It is proven that the proposed method is accurate, fast and cost efficient for detecting and classifying harmonic signals in distribution system.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
Online aptitude test management system project report.pdfKamal Acharya
The purpose of on-line aptitude test system is to take online test in an efficient manner and no time wasting for checking the paper. The main objective of on-line aptitude test system is to efficiently evaluate the candidate thoroughly through a fully automated system that not only saves lot of time but also gives fast results. For students they give papers according to their convenience and time and there is no need of using extra thing like paper, pen etc. This can be used in educational institutions as well as in corporate world. Can be used anywhere any time as it is a web based application (user Location doesn’t matter). No restriction that examiner has to be present when the candidate takes the test.
Every time when lecturers/professors need to conduct examinations they have to sit down think about the questions and then create a whole new set of questions for each and every exam. In some cases the professor may want to give an open book online exam that is the student can take the exam any time anywhere, but the student might have to answer the questions in a limited time period. The professor may want to change the sequence of questions for every student. The problem that a student has is whenever a date for the exam is declared the student has to take it and there is no way he can take it at some other time. This project will create an interface for the examiner to create and store questions in a repository. It will also create an interface for the student to take examinations at his convenience and the questions and/or exams may be timed. Thereby creating an application which can be used by examiners and examinee’s simultaneously.
Examination System is very useful for Teachers/Professors. As in the teaching profession, you are responsible for writing question papers. In the conventional method, you write the question paper on paper, keep question papers separate from answers and all this information you have to keep in a locker to avoid unauthorized access. Using the Examination System you can create a question paper and everything will be written to a single exam file in encrypted format. You can set the General and Administrator password to avoid unauthorized access to your question paper. Every time you start the examination, the program shuffles all the questions and selects them randomly from the database, which reduces the chances of memorizing the questions.
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.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
2. Madhusudana C. K., Hemantha Kumar, Narendranath S/ Journal of Vibration Analysis, Measurement, and Control
(2016) Vol. 4 No. 1 pp. 10-28
11
works based on the acquired process parameters such as current signal, acoustic emission (AE)
signal, cutting force, vibration signal, etc. to determine the condition of the cutting tool. Among
these acquired signals, vibration gives better results about tool condition. One of the advantages of
vibration signal measurement is that no modifications are required for experimental setup (Teti et
al., 2010).
In signal processing techniques, no matter which process parameters are selected, signal processing
techniques such as time domain, frequency domain and time-frequency domain analyses are very
much useful to predict tool condition. Abouelatta and Madl (2001) correlated the surface profile of
workpiece with the cutting parameters and cutting tool vibrations. Huang et al. (2012) investigated
the stable and chatter machining processes through spectrum plots of cutting force and vibrational
signals. Bisu et al. (2012) examined the dynamic behavior of the milling process to monitor the
condition of the cutting tool through spectrum analysis using vibration signals. Sivasakthivel et al.
(2011) developed a mathematical model with process parameters to analyze the vibration
amplitude in high speed end milling of Al 6063 material using spectrum analysis. Antonialli et al.
(2010) analyzed the variations in cutting force during milling of titanium alloy in time and
frequency domain analyses.
From the past two decades, wavelet analysis has become one of the emerging and efficient tools for
identifying the faults in signal processing and has its distinct merits. Some of the wavelet
applications such as the time-frequency domain, denoising of weak signals and extraction of
features related to faults, vibration signals compression, singularity detection for signals, etc. were
used in machine condition monitoring and fault diagnostics (Peng and Chu, 2004). Mori et al. (1999)
analyzed the transient responses in cutting force signals during drilling process using discrete
wavelet transform (DWT) instead of using traditional spectrum analysis. Spectrum analysis has its
own limitations (Zhu et al., 2009) as it can only be used for stationary signals. Li et al. (2005)
revealed that the fast algorithm of wavelet transform is more reliable, sensitive and faster than
spectrum analysis in prediction of tool wear condition during turning process. Li and Guan (2004)
analyzed the feed motor current signals to predict cutting edge fracture through time-frequency
plots in end milling process. Lee and Tarng (1999) determined milling tool breakage through
discrete wavelet transform (DWT) using cutting force signals. Hsieh et al. (2012) studied the
micro-milling tool condition monitoring using vibration signals and proposed a classifier to monitor
the tool condition with the help of relevant features extracted from the vibration signals. Yao et al.
(2010) applied wavelet transform for chatter detection and support vector machine (SVM)
technique for pattern classification during boring process using vibration signals.
Limited literature is available regarding the usage of advanced signal processing techniques such as
wavelet and cepstrum analysis in milling tool condition monitoring. This study aims to analyze the
3. Madhusudana C. K., Hemantha Kumar, Narendranath S/ Journal of Vibration Analysis, Measurement, and Control
(2016) Vol. 4 No. 1 pp. 10-28
12
spindle vibration signals of healthy and faulty conditions of face milling cutter using signal
processing techniques for tool condition monitoring. The conventional vibration analyses such as
time-domain, frequency domain, quefrency domain and advanced signal processing technique such
as CWT method have been used to predict the tool conditions.
2. Signal Processing Technique
2.1 Time and Frequency Domain Analysis
Time domain plot helps to examine the amplitude and phase information of the vibration signal to
determine the failure/defect of any rotating machinery system. Fault diagnosis using time series
response is a difficult task. Fourier transform (FT) is the most widely used technique in vibration
signal analysis. It converts given signal from time domain to frequency domain by integrating the
given function over the entire time period. Fourier transform for the angular frequency 𝜔 =
2𝜋𝑓 and time ‘t’ is given by,
𝑋(⍵) = ∫ 𝑥(𝑡)
+∞
−∞
𝑒−𝑗⍵𝑡
𝑑𝑡 (1)
Where X(ω) is the Fourier transform of the signal x(t). FT technique earned much of its importance
in processing stationary signal. Fast Fourier transform (FFT) is one of the extension of FT (Vernekar
et al., 2014).
In milling process, one of the reasons for vibration of the cutting tool is due to variation in the
cutting force. This cutting force signal is periodic and its variation frequency is tooth passing
frequency (TPF), which depends on spindle rotating frequency (fs) and number of teeth in the
cutting tool. Spindle rotating frequency ‘fs’ is defined as,
𝑓𝑠 =
𝑁
60
=
1000𝑣
60𝜋𝐷
(2)
where D is the diameter of the mill, N is the spindle speed (in revolutions per minute) and v is linear
speed (in meters per minute) . TPF is defined as,
𝑇𝑃𝐹 = 𝑁 𝑇 ∗ 𝑓𝑠 =
1000𝑣 𝑁 𝑇
60𝜋𝐷
(3)
Where 𝑁 𝑇 is the teeth numbers of the cutter, while the presence of peaks at additional
frequencies represents the chatter. This TPF of milling dynamics is often used for detection of the
4. Madhusudana C. K., Hemantha Kumar, Narendranath S/ Journal of Vibration Analysis, Measurement, and Control
(2016) Vol. 4 No. 1 pp. 10-28
13
chatter (Huang et al., 2013).
2.2 Cepstrum Analysis
A cepstrum is considered as forward Fourier transformation of the logarithm of a spectrum. It is
therefore defined as the spectrum of a spectrum. The cepstrum was originally referred as the power
spectrum of the logarithmic power spectrum. Thus, the calculation of cepstrum involves the inverse
Fourier transform of the natural logarithm of a spectrum (Randall, 1982). Given a real signal x(n),
cepstrum form can be expressed as follows.
The real cepstrum of a signal x(n) (Hasegawa, 2000):
𝑐(𝑛) =
1
2𝜋
∫ 𝑙𝑜𝑔
𝜋
−𝜋
|𝑋(𝑒 𝑗𝜔
)|𝑒 𝑗𝜔𝑛
𝑑𝜔 (4)
Where n is cepstral ‘lag’, if x(n) is real, then log|𝑋(𝑒 𝑗𝜔
)| is even. Cepstrum reveals the periodicity
in frequency domain usually as results of modulation. Fig. 1 depicts relationship between spectrum
and cepstrum.
Fig. 1. The relationship between a spectrum and a cepstrum
2.3 Wavelet Analysis
The Fourier transform is not suitable for analyzing non-stationary signals since it fails to reveal the
frequency content of a signal at a particular time. In signal processing, the limitation of FT led to the
introduction of new time-frequency analysis called wavelet transform (WT) (Vernekar et al., 2014).
Generally, conventional data processing is computed in time or frequency domain. Wavelet
processing method combines both time and frequency informations. Wavelet analysis is one of the
‘time-frequency’ analysis. A wavelet is a basis function characterized by two aspects; first is its
shape and amplitude, which is chosen by the user, second is its scale (frequency) and time (location)
relative to the signal.
FFT log (FFT) FFT
CepstrumSpectrum
Signal
5. Madhusudana C. K., Hemantha Kumar, Narendranath S/ Journal of Vibration Analysis, Measurement, and Control
(2016) Vol. 4 No. 1 pp. 10-28
14
The continuous wavelet transform can be used to generate spectrograms which show the frequency
content of signals as a function of time. A continuous-time wavelet transform of x(t) is defined as,
𝐶𝑊𝑇 𝑋 𝜓(𝑎, 𝑏) =
1
√|𝑎|
∫ 𝑥(𝑡)𝜓∗
(
𝑡 − 𝑏
𝑎
) 𝑑𝑡,
∞
−∞
{𝑎, 𝑏 𝜖 𝑅, 𝑎 ≠ 0} (5)
In the above equation (5), ψ(t) is a continuous wavelet function in time domain as well as the
frequency domain called the mother wavelet and ψ*(t) indicates complex conjugate of the analyzing
wavelet ψ(t). The parameter ‘a’ is termed as scaling parameter and ‘b’ is the translation parameter.
The transformed signal Xψ(a, b) is a function of the translation parameter ‘b’ and the scale
parameter ‘a’. In WT, signal energy is normalized by dividing the wavelet coefficients by
1
√|𝑎|
at each
scale.
Morlet Wavelet
The Morlet wavelet transform belongs to CWT family. It is one of the most popular wavelet used in
practice and its mother wavelet is given by,
𝜓(𝑡) =
1
√ 𝜋
4 (𝑒 𝑗𝑤0 𝑡
− 𝑒−
𝑤0
2
2 ) 𝑒−
𝑡2
2 (6)
In the above equation (6), w0 refers to central frequency of the mother wavelet. The term 𝑒−
𝑤0
2
2
involved in the equation is specifically used for correcting the non-zero mean of the complex
sinusoid and in most cases, it can be negligible when w0 > 5. Therefore, when the central frequency
w0 >5, the mother wavelet can be redefined as follows (Vernekar et al., 2014);
𝜓(𝑡) =
1
√ 𝜋
4 𝑒 𝑗𝑤0 𝑡
∗ 𝑒−
𝑡2
2 (7)
3. Experimental Setup
Experiments were carried out using universal milling machine with machining parameters
recommended by Mitsubishi as mentioned in Table 1. Experimental setup consists of universal
milling machine with data acquisition system as shown in Fig. 2. Face milling cutter with 3 carbide
inserts (Mitsubishi make: SEMT13T3AGSN- VP15TF) of 80 mm diameter and work-piece material
of AISI H13 steel were used in this work.
6. Madhusudana C. K., Hemantha Kumar, Narendranath S/ Journal of Vibration Analysis, Measurement, and Control
(2016) Vol. 4 No. 1 pp. 10-28
15
Fig. 2. Experimental setup
Table 1 Experimental condition of face milling process
Experimental Condition
Work material AISI H13 steel
Insert material Carbide
Cutting speed 75 m/min
Feed rate 0.067 mm/tooth
Depth of cut 0.5 mm
Faulty conditions of the tool Flank wear, chipping and breakage
Lubrication Dry
Experiments were conducted with four different conditions of the tool (Fig. 3), out of which one is
healthy and three fault conditions, namely;
a) Healthy tool [Fig. 3(a)]
b) Flank wear (one insert) [Fig. 3(b)]
c) Chipping on rake face (one insert) [Fig. 3(c)]
d) Tip breakage (one insert) [Fig. 3(d)]
7. Madhusudana C. K., Hemantha Kumar, Narendranath S/ Journal of Vibration Analysis, Measurement, and Control
(2016) Vol. 4 No. 1 pp. 10-28
16
a) Healthy b) Flank wear c) Chipping d) Breakage
Fig. 3. Different conditions of face milling tool insert
In healthy condition (Fig. 3a) of the tool, all three inserts are new/unworn inserts, whereas in faulty
condition of the tool, one out of three inserts is either flank wear or chipping or breakage (Fig. 3(b)
or 3(c) or 3(d)) condition has been considered for analysis.
Fig. 4. Schematic representation of condition monitoring of face milling cutter.
Vibrational signals were acquired using tri-axial piezoelectric accelerometer (YMC145A100) which
Vibration signals Cepstrum
0.00 0.02 0.04 0.06 0.08
-0.02
-0.01
0.00
0.01
0.02
0.03
0.04
0.05
0.06
Acceleration
quefrency
Spectrum
0 50 100 150 200
0.00
0.01
0.02
0.03
0.04
Acceleration
Frequency
CWT
Milling cutter condition
(a) healthy, (b) flank
wear, (c) chipping and
(d) breakage
Fault detection
of face milling
cutter condition
8. Madhusudana C. K., Hemantha Kumar, Narendranath S/ Journal of Vibration Analysis, Measurement, and Control
(2016) Vol. 4 No. 1 pp. 10-28
17
was mounted on spindle housing. Data acquisition system (National Instruments DAQ 9234) was
used to acquire the acceleration signals from the sensor with sampling frequency of 5 kHz and these
signals were then processed by LabVIEW software and data was saved. Initially, rough machining
was carried out with few passes to remove the oxidized layer and unevenness of the work-piece.
The process was kept running for two or three minutes to stabilize the machine vibration before
starting data acquisition. The procedure for fault detection of face milling tool using different signal
processing techniques is as shown in Fig. 4.
4. Results and Discussion
4.1 Time-Domain Analysis
(a) (b)
(c) (d)
Fig. 5. Time-series plots of (a) healthy, (b) flank wear, (c) chipping and (d) breakage face milling
tool conditions
0.0 0.2 0.4 0.6 0.8 1.0
-25
-20
-15
-10
-5
0
5
10
15
20
25
Acceleration(g)
Time (sec)
0.0 0.2 0.4 0.6 0.8 1.0
-25
-20
-15
-10
-5
0
5
10
15
20
25
acceleration(g)
Time (sec)
0.0 0.2 0.4 0.6 0.8 1.0
-25
-20
-15
-10
-5
0
5
10
15
20
25
Acceleration(g)
Time (sec)
0.0 0.2 0.4 0.6 0.8 1.0
-25
-20
-15
-10
-5
0
5
10
15
20
25
Acceleration(g)
Time (sec)
9. Madhusudana C. K., Hemantha Kumar, Narendranath S/ Journal of Vibration Analysis, Measurement, and Control
(2016) Vol. 4 No. 1 pp. 10-28
18
The acceleration signals were acquired for healthy and different faulty conditions of the tool. Fig. 5
shows the time-series plots in feed direction for different conditions (healthy, flank wear, chipping
and breakage) of the milling tool. In time domain analysis, slight variations in amplitude of vibration
patterns is observed, but it is very difficult to recognize the different condition of the tool. It means
time domain analysis does not give sufficient information about tool condition.
4.2 Spectrum Analysis
The experimental results of spectrum for different condition of the tool are shown in Fig. 6. From
spectrum plot, under normal cutting condition in a milling process, the dominant frequency
components in the spectrum graph are around the spindle rotating frequency (fs), tooth passing
frequency (TPF) and their harmonics (Orhan et al., 2007).
0 200 400 600 800 1000
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
(a) Healthy
Acceleration(g)
Frequency (Hz)
10. Madhusudana C. K., Hemantha Kumar, Narendranath S/ Journal of Vibration Analysis, Measurement, and Control
(2016) Vol. 4 No. 1 pp. 10-28
19
0 200 400 600 800 1000
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
x: 810 Hz
y: 0.1 g
(b) Flank wear
Acceleration(g)
Frequency (Hz)
0 200 400 600 800 1000
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
x: 810 Hz
y: 0.125 g
(c) Chipping
Acceleration(g)
Frequency (Hz)
11. Madhusudana C. K., Hemantha Kumar, Narendranath S/ Journal of Vibration Analysis, Measurement, and Control
(2016) Vol. 4 No. 1 pp. 10-28
20
Fig. 6. Spectrum plots (a) healthy, (b) flank wear, (c) chipping and (d) breakage
Table 2 Characteristic vibration frequency of spindle speed running at 300 rpm.
Parameters Value
Spindle frequency (fs) 5 Hz
Tooth pass frequency (TPF) 15 Hz
Total number of inserts in milling
tool
3
Table 2 shows the characteristic vibration frequency of milling process with spindle speed running
at 300 rpm. Tooth pass frequency for the given spindle speed and tool inserts is about 15 Hz. It can
be noticed from spectrum plot that along with tooth pass frequency and its harmonics (1x, 2x,
3x,….etc.), few peaks corresponding to chatter are also present. Figs. 6 (a) and (b) show the
spectrum of healthy and flank wear conditions of the tool respectively, 54th multiple of tooth passing
frequency (810 Hz) shows the dominancy among all other harmonics. The corresponding
acceleration amplitude of 54th multiple of TPF is about 0.1 m/s2. This signifies the presence of fault
in the milling tool. The increase in the amplitude level of same frequency (54th multiple of TPF) with
increase in severity of fault (chipping) can be visualized in spectrum as illustrated in Fig. 6(c). The
0 200 400 600 800 1000
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
x: 810 Hz
y: 0.1 g
(d) Breakage
Acceleration(g)
Frequency (Hz)
12. Madhusudana C. K., Hemantha Kumar, Narendranath S/ Journal of Vibration Analysis, Measurement, and Control
(2016) Vol. 4 No. 1 pp. 10-28
21
magnitude of acceleration is increased from 0.1 to 0.125 m/s2, which signifies the increase of fault
level in the milling tool. Also for breakage condition, 54th TPF is the dominant frequency among all
TPF harmonics. It might be evident that, 54th multiple of TPF coincides with the natural frequency
(810 Hz) of tool-workpiece material structure. The cepstrum analysis has been carried out in order
to recognize the tool conditions and also to validate the results of spectrum analysis.
4.3 Cepstrum Analysis
The cepstrum plots of face milling tool under different conditions (healthy, flank wear, chipping and
breakage) are as shown in Fig. 7. As discussed in spectrum analysis, the dominant peak
corresponding to 810 Hz (54th multiple of TPF) is the defect frequency. In cepstrum analysis, the
defect frequency is called as defect quefrency of about 0.0012s (1/810Hz) which shows the
variation in amplitude of acceleration for different conditions of the tool. Fig. 7(a) shows the
cepstrum plot of a healthy tool where the acceleration of dominant peak at quefrency (0.0012s) is
about 0.015 m/s2 which is to be considered as a reference margin for fault detection. As the faults
(flank wear, chipping and breakage) are introduced into the milling tool, the acceleration value at
defect quefrency (0.0012s) is increased. In case of flank wear condition (Fig. 7(b)), 54th multiples of
tooth passing quefrency (0.0012s) has the acceleration of about 0.031 m/s2, which implies the
presence of faults in the milling tool. For chipping condition (Fig. 7(c)), the acceleration value at
quefrency (0.0012s) is about 0.04 m/s2, which signifies the increase in the level of faults during
milling process. In case of breakage tool condition, the acceleration at defect quefrency is about
0.035 m/s2 as shown in Fig. 7(d).
From the above discussion of spectrum and cepstrum analyses of the face milling tool, it can be
visualized that even with the presence of defect in the tool, it is quite difficult to identify the
particular time at which the defect frequency/quefrency is being attained and it requires
time-frequency domain analysis. Wavelet analysis demonstrates both time and frequency domains,
meaning that it generates frequency content signals as a function of time.
14. Madhusudana C. K., Hemantha Kumar, Narendranath S/ Journal of Vibration Analysis, Measurement, and Control
(2016) Vol. 4 No. 1 pp. 10-28
23
Fig. 7. Cepstrum plots (a) healthy, (b) flank wear, (c) chipping and (d) breakage conditions.
0.00 0.02 0.04 0.06 0.08
-0.02
-0.01
0.00
0.01
0.02
0.03
0.04
0.05
0.06
x: 0.00124 sec
y: 0.04 g
(c) Chipping
Acceleration(g)
Quefrency (sec)
0.00 0.02 0.04 0.06 0.08
-0.02
-0.01
0.00
0.01
0.02
0.03
0.04
0.05
0.06
x: 0.00124 sec
y: 0.035 g
(d) Breakage
Acceleration(g)
Quefrency (sec)
15. Madhusudana C. K., Hemantha Kumar, Narendranath S/ Journal of Vibration Analysis, Measurement, and Control
(2016) Vol. 4 No. 1 pp. 10-28
24
4.4 Wavelet Analysis
Fig. 8 illustrates CWT plots of the milling machine spindle vibration with healthy and fault
conditions of the face milling tool.
(c) Chipping
(a) Healthy
(b) Flank wear
16. Madhusudana C. K., Hemantha Kumar, Narendranath S/ Journal of Vibration Analysis, Measurement, and Control
(2016) Vol. 4 No. 1 pp. 10-28
25
Fig. 8. CWT plots of (a) healthy, (b) flank wear, (c) chipping and (d) breakage
From Fig. 8, one can say that for the given time period (one second) there is some variation in
intensity of high frequency band at 810 Hz, as the faults occur in the milling tool. The presence of
high-frequency component at 810 Hz (54th multiple of TPF) which is one of the harmonics of TPF.
Fig. 8 (a) depicts the CWT plot of healthy condition as a reference margin for fault detection. As the
faults such as flank wear, chipping and breakage occur in milling tool, intensity of the high
frequency (810 Hz) band has been increased as shown in Fig. 8(b), (c) and (d). This variations in
intensity of high frequency band indicate the fault existence in milling tool.
5. Conclusion
In this paper, signal processing techniques such as spectrum analysis, cepstrum analysis and CWT
were used to analyze the vibration signals under healthy and faulty conditions for identifying the
faults in face milling tool. As seen from the plots of spectrum, cepstrum and CWT techniques, as the
severity of fault increases, there is a dominant peak at 54th multiples of TPF and it nearly coincides
with the natural frequency (about 810 Hz) of tool-workpiece material structure. This signifies the
faults occurring in milling tool for the given workpiece material and process condition. Based on the
experimental results, following conclusions are drawn.
Time-series plots provide insufficient diagnostic information in vibration signals of different
tool condition.
Spectrum plots are used to detect faults in milling tool, it only gives information about
frequency component of vibration signals as a dominant peak and does not provide time
information about faults.
In cepstrum plots, it is very much useful to assess defect quefrency of milling tool and it was
observed that amplitude of this quefrency varies with the increase in fault level.
CWT plots with vibration signals provide enough information about faults in milling tool in
(d) Breakage
17. Madhusudana C. K., Hemantha Kumar, Narendranath S/ Journal of Vibration Analysis, Measurement, and Control
(2016) Vol. 4 No. 1 pp. 10-28
26
both time and frequency domain.
Based on the results obtained, it can be judged that cepstrum and CWT are recommended for
practical applications in fault detection of the face milling tool.
Acknowledgement
The authors acknowledge the support from SOLVE: The Virtual Lab @ NITK, Surathkal
(www.solve.nitk.ac.in) for providing experimental facility.
References
Abouelatta, O. B., and Madl, J. (2001). Surface roughness prediction based on cutting parameters and tool
vibrations in turning operations. Journal of materials processing technology, 118(1), 269-277.
http://dx.doi.org/10.1016/S0924-0136(01)00959-1
Antonialli, A. I. S., Diniz, A. E., and Pederiva, R. (2010). Vibration analysis of cutting force in titanium alloy
milling. International Journal of Machine Tools and Manufacture, 50(1), 65-74.
http://dx.doi.org/10.1016/j.ijmachtools.2009.09.006
Bisu, C. F., Zapciu, M., Cahuc, O., Gérard, A., and Anica, M. (2012). Envelope dynamic analysis: a new approach
for milling process monitoring. The International Journal of Advanced Manufacturing Technology, 62(5-8),
471-486.
http://dx.doi.org/10.1007/s00170-011-3814-4
Hasegawa-Johnson, M. (2000). Lecture notes in speech production, speech coding, and speech recognition.
Class notes, University of Illinois at Urbana-Champaign, Fall, Chicago.
Hsieh, W. H., Lu, M. C., and Chiou, S. J. (2012). Application of backpropagation neural network for spindle
vibration-based tool wear monitoring in micro-milling. The International Journal of Advanced
Manufacturing Technology, 61(1-4), 53-61.
http://dx.doi.org/10.1007/s00170-011-3703-x
Huang, P., Li, J., Sun, J., and Ge, M. (2012). Milling force vibration analysis in high-speed-milling titanium alloy
using variable pitch angle mill. The International Journal of Advanced Manufacturing Technology, 58(1-4),
153-160.
http://dx.doi.org/10.1007/s00170-011-3380-9
Huang, P., Li, J., Sun, J., and Zhou, J. (2013). Vibration analysis in milling titanium alloy based on signal
processing of cutting force. The International Journal of Advanced Manufacturing Technology, 64(5-8),
613-621.
http://dx.doi.org/10.1007/s00170-012-4039-x
18. Madhusudana C. K., Hemantha Kumar, Narendranath S/ Journal of Vibration Analysis, Measurement, and Control
(2016) Vol. 4 No. 1 pp. 10-28
27
Lee, B. Y., and Tarng, Y. S. (1999). Milling cutter breakage detection by the discretewavelet transform.
Mechatronics, 9(3), 225-234.
http://dx.doi.org/10.1016/S0957-4158(98)00049-X
Li, W., Gong, W., Obikawa, T., and Shirakashi, T. (2005). A method of recognizing tool-wear states based on a fast
algorithm of wavelet transform. Journal of materials processing technology, 170(1), 374-380.
http://dx.doi.org/10.1016/j.jmatprotec.2005.05.018
Li, X., and Guan, X. P. (2004). Time-frequency-analysis-based minor cutting edge fracture detection during end
milling. Mechanical Systems and Signal Processing, 18(6), 1485-1496.
http://dx.doi.org/10.1016/S0888-3270(03)00096-7
Mori, K., Kasashima, N., Fu, J. C., and Muto, K. (1999). Prediction of small drill bit breakage by wavelet
transforms and linear discriminant functions. International Journal of Machine Tools and Manufacture,
39(9), 1471-1484.
http://dx.doi.org/10.1016/S0890-6955(99)00004-8
Orhan, S., Er, A. O., Camuşcu, N., and Aslan, E. (2007). Tool wear evaluation by vibration analysis during end
milling of AISI D3 cold work tool steel with 35 HRC hardness. NDT and E International, 40(2), 121-126.
http://dx.doi.org/10.1016/j.ndteint.2006.09.006
Peng, Z. K., and Chu, F. L. (2004). Application of the wavelet transform in machine condition monitoring and
fault diagnostics: a review with bibliography. Mechanical systems and signal processing, 18(2), 199-221.
http://dx.doi.org/10.1016/S0888-3270(03)00075-X
Randall, R. B. (1982). Cepstrum analysis and gearbox fault-diagnosis. Maintenance Management International,
3(3), 183-208.
Sivasakthivel, P. S., Velmurugan, V., and Sudhakaran, R. (2011). Prediction of vibration amplitude from
machining parameters by response surface methodology in end milling. The International Journal of
Advanced Manufacturing Technology, 53(5-8), 453-461.
http://dx.doi.org/10.1007/s00170-010-2872-3
Teti, R., Jemielniak, K., O'Donnell, G., and Dornfeld, D. (2010). Advanced monitoring of machining operations.
CIRP Annals-Manufacturing Technology, 59(2), 717-739.
http://dx.doi.org/10.1016/j.cirp.2010.05.010
Vernekar, K., Kumar, H., and Gangadharan, K. V. (2014). Gear Fault Detection Using Vibration Analysis and
Continuous Wavelet Transform. Procedia Materials Science, 5, 1846-1852.
http://dx.doi.org/10.1016/j.mspro.2014.07.492
Yao, Z., Mei, D., and Chen, Z. (2010). On-line chatter detection and identification based on wavelet and support
vector machine. Journal of Materials Processing Technology, 210(5), 713-719.
http://dx.doi.org/10.1016/j.jmatprotec.2009.11.007
Zhu, K., San Wong, Y., and Hong, G. S. (2009). Wavelet analysis of sensor signals for tool condition monitoring: a
review and some new results. International Journal of Machine Tools and Manufacture, 49(7), 537-553.
19. Madhusudana C. K., Hemantha Kumar, Narendranath S/ Journal of Vibration Analysis, Measurement, and Control
(2016) Vol. 4 No. 1 pp. 10-28
28
http://dx.doi.org/10.1016/j.ijmachtools.2009.02.003
nd Manufacture, 49(7), 537-553.