Condition Monitoring and Fault Diagnosis of Roller Element Bearing

Rolling element bearings play a crucial role in determining the overall health condition of a rotating machine. An effective condition-monitoring program on bearing operation can improve a machine’s operation efficiency, reduce the maintenance/replacement cost, and prolong the useful lifespan of a machine. This chapter presents a general overview of various condition-monitoring and fault diagnosis techniques for rolling element bearings in the current practice and discusses the pros and cons of each technique. The techniques introduced in the chapter include data acquisition techniques, major parameters used for bearing condition monitoring, signal analysis techniques, and bearing fault diagnosis techniques using either statistical features or artificial intelligent tools. Several case studies are also presented in the chapter to exemplify the application of these techniques in the data analysis as well as bearing fault diagnosis and pattern recognition.

[1]  N. Huang,et al.  A study of the characteristics of white noise using the empirical mode decomposition method , 2004, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[2]  Dong Yu,et al.  Deep Learning and Its Applications to Signal and Information Processing [Exploratory DSP] , 2011, IEEE Signal Processing Magazine.

[3]  Lawrence D. Jackel,et al.  Handwritten Digit Recognition with a Back-Propagation Network , 1989, NIPS.

[4]  Laibin Zhang,et al.  Research on fault diagnosis for reciprocating compressor valve using information entropy and SVM method , 2009 .

[5]  Cong Wang,et al.  Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings , 2016 .

[6]  Robert B. Randall,et al.  Rolling element bearing diagnostics—A tutorial , 2011 .

[7]  Xiaofei He,et al.  Locality Preserving Projections , 2003, NIPS.

[8]  Yaguo Lei,et al.  Application of an improved kurtogram method for fault diagnosis of rolling element bearings , 2011 .

[9]  Ruqiang Yan,et al.  A sparse auto-encoder-based deep neural network approach for induction motor faults classification , 2016 .

[10]  Fulei Chu,et al.  Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography , 2004 .

[11]  Tian Ran Lin,et al.  A practical approach to analyze the non-stationary signals of a quayside container crane motor using a combined empirical mode decomposition and wavelet packet quantization technique , 2016 .

[12]  Tian Han,et al.  Fault Diagnosis System of Induction Motors Based on Multiscale Entropy and Support Vector Machine with Mutual Information Algorithm , 2016 .

[13]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[14]  Asoke K. Nandi,et al.  EXTRACTION OF IMPACTING SIGNALS USING BLIND DECONVOLUTION , 2000 .

[15]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[16]  J. Antoni.  The spectral kurtosis: a useful tool for characterising non-stationary signals , 2006 .

[17]  Farid Kadri,et al.  Seasonal ARMA-based SPC charts for anomaly detection: Application to emergency department systems , 2016, Neurocomputing.

[18]  Ming Liang,et al.  Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications , 2016 .

[19]  고봉환,et al.  Minimum Entropy Deconvolution을 이용한 회전체 시스템의 결함 모니터링 , 2013 .

[20]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[21]  Tian Ran Lin,et al.  Normalization and source separation of acoustic emission signals for condition monitoring and fault detection of multi-cylinder diesel engines , 2015 .

[22]  Robert B. Randall,et al.  The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines , 2006 .

[23]  Chen Lu,et al.  Self-adaptive bearing fault diagnosis based on permutation entropy and manifold-based dynamic time warping , 2016, Mechanical Systems and Signal Processing.

[24]  Abraham Lempel,et al.  Compression of individual sequences via variable-rate coding , 1978, IEEE Trans. Inf. Theory.

[25]  Dong Yu,et al.  Deep Learning and Its Applications to Signal and Information Processing , 2011 .

[26]  Yoshua Bengio,et al.  Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.

[27]  C. M. Lim,et al.  Application of higher order statistics/spectra in biomedical signals--a review. , 2010, Medical engineering & physics.

[28]  Hua-Shu Dou,et al.  Vibration-Based Condition Monitoring , 2013 .

[29]  Ming Liang,et al.  Fault severity assessment for rolling element bearings using the Lempel–Ziv complexity and continuous wavelet transform , 2009 .

[30]  ZhiQiang Chen,et al.  Gearbox Fault Identification and Classification with Convolutional Neural Networks , 2015 .

[31]  Abraham Lempel,et al.  On the Complexity of Finite Sequences , 1976, IEEE Trans. Inf. Theory.

[32]  H. Sebastian Seung,et al.  The Manifold Ways of Perception , 2000, Science.

[33]  Tian Ran Lin,et al.  A practical signal processing approach for condition monitoring of low speed machinery using Peak-Hold-Down-Sample algorithm , 2012 .

[34]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[35]  Lei Deng,et al.  Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine , 2014 .

[36]  Yaguo Lei,et al.  A review on empirical mode decomposition in fault diagnosis of rotating machinery , 2013 .

[37]  Xuezhi Zhao,et al.  Similarity of signal processing effect between Hankel matrix-based SVD and wavelet transform and its mechanism analysis , 2009 .