Bearing fault recognition method based on neighbourhood component analysis and coupled hidden Markov model

Abstract Due to the important role rolling element bearings play in rotating machines, condition monitoring and fault diagnosis system should be established to avoid abrupt breakage during operation. Various features from time, frequency and time–frequency domain are usually used for bearing or machinery condition monitoring. In this study, NCA-based feature extraction (FE) approach is proposed to reduce the dimensionality of original feature set and avoid the “curse of dimensionality”. Furthermore, coupled hidden Markov model (CHMM) based on multichannel data acquisition is applied to diagnose bearing or machinery fault. Two case studies are presented to validate the proposed approach both in bearing fault diagnosis and fault severity classification. The experiment results show that the proposed NCA-CHMM can remove redundant information, fuse data from different channels and improve the diagnosis results.

[1]  P. D. McFadden,et al.  Model for the vibration produced by a single point defect in a rolling element bearing , 1984 .

[2]  Jérôme Antoni,et al.  Vibration based condition monitoring of a multistage epicyclic gearbox in lifting cranes , 2014 .

[3]  Sanjib Kumar Panda,et al.  Fault detection and diagnosis in synchronous motors using hidden Markov model-based semi-nonparametric approach , 2013, Eng. Appl. Artif. Intell..

[4]  Ruxu Du,et al.  Fault diagnosis of stamping process based on empirical mode decomposition and learning vector quantization , 2007 .

[5]  Yuesheng Xu,et al.  Gearbox fault diagnosis using empirical mode decomposition and Hilbert spectrum , 2006 .

[6]  M. S. Safizadeh,et al.  Using multi-sensor data fusion for vibration fault diagnosis of rolling element bearings by accelerometer and load cell , 2014, Inf. Fusion.

[7]  Jianbo Yu,et al.  Bearing performance degradation assessment using locality preserving projections and Gaussian mixture models , 2011 .

[8]  Nagi Gebraeel,et al.  Residual life predictions from vibration-based degradation signals: a neural network approach , 2004, IEEE Transactions on Industrial Electronics.

[9]  S. Marble,et al.  Validating Prognostic Algorithms: A Case Study Using Comprehensive Bearing Fault Data , 2007, 2007 IEEE Aerospace Conference.

[10]  Bo-Suk Yang,et al.  Application of Dempster–Shafer theory in fault diagnosis of induction motors using vibration and current signals , 2006 .

[11]  Guangming Dong,et al.  A multichannel fusion approach based on coupled hidden Markov models for rolling element bearing fault diagnosis , 2012 .

[12]  Govindappa Krishnappa,et al.  Bearing Diagnostics Based on Pattern Recognition of Statistical Parameters , 2000 .

[13]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[14]  Timothy J. Hazen,et al.  Dimensionality reduction for speech recognition using neighborhood components analysis , 2007, INTERSPEECH.

[15]  Kevin P. Murphy,et al.  Dynamic Bayesian Networks for Audio-Visual Speech Recognition , 2002, EURASIP J. Adv. Signal Process..

[16]  George Nikolakopoulos,et al.  Principal Component Analysis of the start-up transient and Hidden Markov Modeling for broken rotor bar fault diagnosis in asynchronous machines , 2013, Expert Syst. Appl..

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

[18]  Lifeng Xi,et al.  Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods , 2007 .

[19]  Li Bai,et al.  Face Verification Using Indirect Neighbourhood Components Analysis , 2010, ISVC.

[20]  Alex Pentland,et al.  Coupled hidden Markov models for complex action recognition , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[21]  Tianming Liu,et al.  Bearing Performance Degradation Assessment Using Linear Discriminant Analysis and Coupled HMM , 2012 .

[22]  Geoffrey E. Hinton,et al.  Neighbourhood Components Analysis , 2004, NIPS.