Access the full text.
Sign up today, get DeepDyve free for 14 days.
F. Nelwamondo, T. Marwala, Unathi Mahola (2006)
EARLY CLASSIFICATIONS OF BEARING FAULTS USING HIDDEN MARKOV MODELS, GAUSSIAN MIXTURE MODELS, MEL-FREQUENCY CEPSTRAL COEFFICIENTS AND FRACTALS
Chih-Chung Chang, Chih-Jen Lin (2011)
LIBSVM: A library for support vector machinesACM Trans. Intell. Syst. Technol., 2
D. Griffin, Jae Lim (1983)
Signal estimation from modified short-time Fourier transform
U. Benko, J. Petrovčič, Đ. Juričić, Joža Tavčar, J. Rejec (2005)
An approach to fault diagnosis of vacuum cleaner motors based on sound analysisMechanical Systems and Signal Processing, 19
N. Baydar, A. Ball (2001)
A comparative study of acoustic and vibration signals in detection of gear failures using Wigner-Ville distribution.Mechanical Systems and Signal Processing, 15
M. Portnoff (1980)
Time-frequency representation of digital signals and systems based on short-time Fourier analysisIEEE Transactions on Acoustics, Speech, and Signal Processing, 28
M. Saimurugan, R. Nithesh (2020)
Intelligent Fault Diagnosis Model for Rotating Machinery Based on Fusion of Sound Signals, 7
Zhenyou Zhang, Yi Wang, Ke-sheng Wang (2013)
Fault diagnosis and prognosis using wavelet packet decomposition, Fourier transform and artificial neural networkJournal of Intelligent Manufacturing, 24
C. Scheer, W. Reimche, F. Bach (2007)
EARLY FAULT DETECTION AT GEAR UNITS BY ACOUSTIC EMISSION AND WAVELET ANALYSIS
Z. Zhong, J. Chen, P. Zhong, J. Wu (2006)
Application of the blind source separation method to feature extraction of machine sound signalsThe International Journal of Advanced Manufacturing Technology, 28
(2009)
International conference on instrumentation, communication, information technology, and biomedical engineering
Abdullah Al-Ghamd, D. Mba (2006)
A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect sizeMechanical Systems and Signal Processing, 20
N. Verma, V. Gupta, Mayank Sharma, R. Sevakula (2013)
Intelligent condition based monitoring of rotating machines using sparse auto-encoders2013 IEEE Conference on Prognostics and Health Management (PHM)
Takaaki Tagawa, Y. Tadokoro, T. Yairi (2014)
Structured Denoising Autoencoder for Fault Detection and Analysis
D. Mba, R. Rao (2006)
Development of Acoustic Emission Technology for Condition Monitoring andDiagnosis of Rotating Machines; Bearings, Pumps, Gearboxes, Engines and RotatingStructures.The Shock and Vibration Digest, 38
P. Comon, C. Jutten (2010)
Handbook of Blind Source Separation: Independent Component Analysis and Applications
Yanxue Wang, J. Xiang, R. Markert, M. Liang (2016)
Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applicationsMechanical Systems and Signal Processing, 66
(2003)
A comparative study of short time fourier transform and continuous wavelet transform for bearing condition monitoring
Wenbo Lu, Weikang Jiang, Guo-qing Yuan, Li Yan (2013)
A gearbox fault diagnosis scheme based on near-field acoustic holography and spatial distribution features of sound fieldJournal of Sound and Vibration, 332
M. Elforjani (2009)
Detecting natural crack initiation and growth in slow speed shafts with the Acoustic Emission technologyEngineering Failure Analysis, 16
S. Qian, Dapang Chen (1994)
Decomposition of the Wigner-Ville distribution and time-frequency distribution seriesIEEE Trans. Signal Process., 42
L Han, J Hong, D Wang (2009)
Fault diagnosis of aeroengine bearings based onwavelet package analysisTuijin Jishu/ J. Propuls. Technol., 30
Xiaoping Liu, Jun Shi, X. Sha, Naitong Zhang (2015)
A general framework for sampling and reconstruction in function spaces associated with fractional Fourier transformSignal Process., 107
A. Albarbar, F. Gu, A. Ball (2010)
Diesel engine fuel injection monitoring using acoustic measurements and independent component analysisMeasurement, 43
L. Clemmensen, T. Hastie, D. Witten, Bjarne Ersbøll (2011)
Sparse Discriminant AnalysisTechnometrics, 53
P. Delgado-Arredondo, D. Morinigo-Sotelo, R. Osornio-Ríos, J. Aviña-Cervantes, H. Rostro-González, R. Romero-Troncoso (2017)
Methodology for fault detection in induction motors via sound and vibration signalsMechanical Systems and Signal Processing, 83
Yan Bing, Qu Weidong (2016)
Aero-engine sensor fault diagnosis based on stacked denoising autoencoders2016 35th Chinese Control Conference (CCC)
Gomaa, D. Khader (2016)
Fault Diagnosis of Rotating Machinery based on vibration analysis
M. Sakurada, T. Yairi (2014)
Anomaly Detection Using Autoencoders with Nonlinear Dimensionality Reduction
Kang Chen, Xiaobing Li, Feng Wang, Tanglin Wang, Cheng Wu (2012)
Bearing fault diagnosis using Wavelet analysis2012 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering
Walace Pacheco, F. Pinto (2014)
Bearing Fault Detection Using Beamforming Technique and Artificial Neural Networks
M. Kumar, P. Mukherjee, N. Misra (2013)
Advancement and current status of wear debris analysis for machine condition monitoring: a reviewIndustrial Lubrication and Tribology, 65
M. Fezari, Z. Taif
Analyzing Emission Sounds : A way for Early Detection of Bearing Faults in Rotating Machines
M. Othman, M. Nuawi, R. Mohamed (2016)
Experimental Comparison of Vibration and Acoustic Emission Signal Analysis Using Kurtosis-Based Methods for Induction Motor Bearing Condition MonitoringPrzegląd Elektrotechniczny, 92
X Lei, PA Sandborn (2016)
PHM-based wind turbine maintenance optimization using real optionsInt. J. Progn. Health Manag., 7
W. Staszewski, K. Worden, G. Tomlinson (1997)
TIME–FREQUENCY ANALYSIS IN GEARBOX FAULT DETECTION USING THE WIGNER–VILLE DISTRIBUTION AND PATTERN RECOGNITIONMechanical Systems and Signal Processing, 11
Bagus Atmaja, D. Arifianto (2009)
Machinery fault diagnosis using independent component analysis (ICA) and Instantaneous Frequency (IF)International Conference on Instrumentation, Communication, Information Technology, and Biomedical Engineering 2009
Tong Zhang (2001)
An Introduction to Support Vector Machines and Other Kernel-Based Learning MethodsAI Mag., 22
Peng Guo, D. Infield, Xiyun Yang (2012)
Wind Turbine Generator Condition-Monitoring Using Temperature Trend AnalysisIEEE Transactions on Sustainable Energy, 3
D. Pandya, S. Upadhyay, S. Harsha (2013)
Fault diagnosis of rolling element bearing with intrinsic mode function of acoustic emission data using APF-KNNExpert Syst. Appl., 40
C. James, D. Lowe (2003)
Extracting multisource brain activity from a single electromagnetic channelArtificial intelligence in medicine, 28 1
(2009)
Processing, 3rd edn
W. Caesarendra, T. Tjahjowidodo, B. Kosasih, A. Tieu (2017)
Integrated condition monitoring and prognosis method for incipient defect detection and remaining life prediction of low speed slew bearings, 5
N. Chandra, A.S. Sekhar (2016)
Fault detection in rotor bearing systems using time frequency techniquesMechanical Systems and Signal Processing, 72
Baudat G, Anouar F (2000)
Generalized Discriminant Analysis Using a Kernel ApproachNeural Computation, 12
F. Bao, Xin Liu, Christina Zhang (2011)
PyEEG: An Open Source Python Module for EEG/MEG Feature ExtractionComputational Intelligence and Neuroscience, 2011
Jinglong Chen, Zipeng Li, Jun Pan, Gaige Chen, Y. Zi, Jing Yuan, Binqiang Chen, Zhengjia He (2016)
Wavelet transform based on inner product in fault diagnosis of rotating machinery: A reviewMechanical Systems and Signal Processing, 70
S. Al-Dossary, R. Hamzah (2009)
Observations of changes in acoustic emission waveform for varying seeded defect sizes in a rolling element bearingApplied Acoustics, 70
C. Li, Jun Ma (1997)
Wavelet decomposition of vibrations for detection of bearing-localized defectsNdt & E International, 30
Deng Xiao-wen, Yang Ping, Ren Jin-sheng, Yang Yi-wei (2014)
Rolling bearings time and frequency domain fault diagnosis method based on Kurtosis analysis2014 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)
D. Gu, Jae-Gu Kim, Young-Su An, Byeong-Keun Choi (2011)
Detection of faults in gearboxes using acoustic emission signalJournal of Mechanical Science and Technology, 25
AV Oppenheim, RW Schafer (2009)
Discrete-Time Signal Processing
P. Lipar, M. Čudina, P. Steblaj, J. Prezelj (2015)
Automatic Recognition of Machinery Noise in the Working EnvironmentStrojniski Vestnik-journal of Mechanical Engineering, 61
Fang Liu, Changqing Shen, Qingbo He, Ao Zhang, Yongbin Liu, Fanrang Kong (2014)
Wayside Bearing Fault Diagnosis Based on a Data-Driven Doppler Effect Eliminator and Transient Model AnalysisSensors (Basel, Switzerland), 14
Akhand Rai, S. Upadhyay (2016)
A review on signal processing techniques utilized in the fault diagnosis of rolling element bearingsTribology International, 96
Amir Atiya (2005)
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and BeyondIEEE Transactions on Neural Networks, 16
P. McFadden, J. Smith (1984)
Vibration monitoring of rolling element bearings by the high-frequency resonance technique — a reviewTribology International, 17
Bin Chen, Zhaoli Yan, Wei Chen (2014)
Defect Detection for Wheel-Bearings with Time-Spectral Kurtosis and EntropyEntropy, 16
D. Baccar, D. Söffker (2015)
Wear detection by means of wavelet-based acoustic emission analysisMechanical Systems and Signal Processing, 60
J. Chacón, V. Kappatos, W. Balachandran, T. Gan (2015)
A novel approach for incipient defect detection in rolling bearings using acoustic emission techniqueApplied Acoustics, 89
N. Baydar, A. Ball (2003)
DETECTION OF GEAR FAILURES VIA VIBRATION AND ACOUSTIC SIGNALS USING WAVELET TRANSFORMMechanical Systems and Signal Processing, 17
Alex Andrew (2001)
An Introduction to Support Vector Machines and Other Kernel‐based Learning MethodsKybernetes, 30
J. Antoni (2007)
Fast computation of the kurtogram for the detection of transient faultsMechanical Systems and Signal Processing, 21
Xin Lei, P. Sandborn (2020)
International Journal of Prognostics and Health Management, ISSN 2153-2648, 2016 008 PHM-Based Wind Turbine Maintenance Optimization Using Real Options, 7
M. Amarnath, V. Sugumaran, H. Kumar (2013)
Exploiting sound signals for fault diagnosis of bearings using decision treeMeasurement, 46
H. Giv (2013)
Directional short-time Fourier transformJournal of Mathematical Analysis and Applications, 399
Bongstik Kim, S. Lee, Moon Lee, J. Ni, J. Song, C. Lee (2007)
A comparative study on damage detection in speed-up and coast-down process of grinding spindle-typed rotor-bearing systemJournal of Materials Processing Technology, 187
(2010)
PatternRecognition
E. Germen, M. Basaran, M. Fidan (2014)
Sound based induction motor fault diagnosis using Kohonen self-organizing mapMechanical Systems and Signal Processing, 46
P. Rodríguez, J. Alonso, M. Ferrer-Ballester, C. Travieso-González (2014)
Review of Automatic Fault Diagnosis Systems Using Audio and Vibration SignalsIEEE Transactions on Systems, Man, and Cybernetics: Systems, 44
R. Vilela, J. Metrôlho, J. Cardoso (2004)
Machine and industrial monitorization system by analysis of acoustic signaturesProceedings of the 12th IEEE Mediterranean Electrotechnical Conference (IEEE Cat. No.04CH37521), 1
Claude Turner, Anthony Joseph (2015)
A Wavelet Packet and Mel-Frequency Cepstral Coefficients-Based Feature Extraction Method for Speaker Identification
Lukasz Jedlinski, J. Jonak (2015)
Early fault detection in gearboxes based on support vector machines and multilayer perceptron with a continuous wavelet transformAppl. Soft Comput., 30
Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations
QH Qin, B Sun (2014)
Advances in Engineering Mechanics and Materials, Chapter Analyzing Emission Sounds: A Way for Early Detection of Bearing Faults in Rotating Machines
Yuan Xie, Zhang Tao (2017)
Fault Diagnosis for Rotating Machinery Based on Convolutional Neural Network and Empirical Mode DecompositionShock and Vibration, 2017
Vibration and acoustic emission have received great attention of the research community for condition-based maintenance in rotating machinery. Several signal processing algorithms were either developed or used efficiently to detect and classify faults in bearings and gears. These signals are recorded, using sensors like tachometer or accelerometer, connected directly or mounted very close to the system under observation. This is not a feasible option in case of complex machinery and/or temperature and humidity. Therefore, it is required to sense the signals remotely, in order to reduce installation and maintenance cost. However, its installation far away from the intended device may pollute the required signal with other unwanted signals. In an attempt to address these issues, sound signal-based fault detection and classification in rotating bearings is presented. In this research work, audible sound of machine under test is captured using a single microphone and different statistical, spectral and spectro-temporal features are extracted. The selected features are then analyzed using different machine learning techniques, such as K-nearest neighbor (KNN) classifier, support vector machine (SVM), kernel liner discriminant analysis (KLDA) and sparse discriminant analysis (SDA). Simulation results show successful classification of faults into ball fault, inner and outer race faults. Best results were achieved using the KLDA followed by SDA, KNN and SVM. As far as features are concerned, the average FFT outperformed all the other features, followed by average PSD, RMS values of PSD, PSD and STFT.
Acoustics Australia – Springer Journals
Published: Mar 27, 2019
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.