BEARING PERFORMANCE DEGRADATION ASSESSMENT BY ORTHOGONAL LOCAL PRESERVING PROJECTION AND CONTINUOUS HIDDEN MARKOV MODEL

Bearing is the key component in rotating machine. It is important to assess the performance degradation degree of bearings for making proactive maintenance and realizing near-zero downtime. A methodology based on orthogonal local preserving projection (OLPP) and continuous hidden Markov model (CHMM) is introduced in bearing performance degradation assessment. Firstly, the time domain, frequency domain and time-frequency domain features are extracted from the vibration signals. Then, the multi-dimensional features are reduced by OLPP. And the selection of the adjacent paragraph parameters in OLPP is optimized adaptively by minimizing the ratio of between-class distance to within-class distance. A CHMM is trained by using the reduced feature in normal condition. At last, the test bearing data are input into the pre-trained CHMM to calculate the log-likelihood of the test data, which can assess the performance degradation of bearings quantitatively. A bearing accelerated life experiment is performed to valid...

[1]  Giansalvo Cirrincione,et al.  Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks , 2013, IEEE Transactions on Industrial Electronics.

[2]  James Hensman,et al.  Natural computing for mechanical systems research: A tutorial overview , 2011 .

[3]  Lei Li,et al.  Rolling Elements Bearings Degradation Indicator Based on Continuous Hidden Markov Model , 2015, Journal of Failure Analysis and Prevention.

[4]  Jay Lee,et al.  A novel method for machine performance degradation assessment based on fixed cycle features test , 2009 .

[5]  Ivan Prebil,et al.  Non-linear multivariate and multiscale monitoring and signal denoising strategy using Kernel Principal Component Analysis combined with Ensemble Empirical Mode Decomposition method , 2011 .

[6]  Noureddine Zerhouni,et al.  A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models , 2012, IEEE Transactions on Reliability.

[7]  Jian-Da Wu,et al.  Fault diagnosis of an automotive air-conditioner blower using noise emission signal , 2010, Expert Syst. Appl..

[8]  Satish C. Sharma,et al.  Fault diagnosis of rolling element bearing using cyclic autocorrelation and wavelet transform , 2013, Neurocomputing.

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

[10]  M. Feldman.  Hilbert transform in vibration analysis , 2011 .

[11]  Benwei Li,et al.  Supervised locally linear embedding projection (SLLEP) for machinery fault diagnosis , 2011 .

[12]  Liangsheng Qu,et al.  Diagnosis of subharmonic faults of large rotating machinery based on EMD , 2009 .

[13]  Bo-Suk Yang,et al.  Application of relevance vector machine and survival probability to machine degradation assessment , 2011, Expert Syst. Appl..

[14]  Qin Li,et al.  Fast kernel Fisher discriminant analysis via approximating the kernel principal component analysis , 2011, Neurocomputing.

[15]  Bo-Suk Yang,et al.  Machine health prognostics using survival probability and support vector machine , 2011, Expert Syst. Appl..

[16]  Z. J. Yang,et al.  Theoretical–experimental study on a rotor with a residual shaft bow , 2013 .

[17]  Jiawei Han,et al.  Document clustering using locality preserving indexing , 2005, IEEE Transactions on Knowledge and Data Engineering.

[18]  Robert X. Gao,et al.  Wavelets for fault diagnosis of rotary machines: A review with applications , 2014, Signal Process..

[19]  F. E. Grubbs.  Procedures for Detecting Outlying Observations in Samples , 1969 .

[20]  Eric O. Postma,et al.  Dimensionality Reduction: A Comparative Review , 2008 .

[21]  Jiawei Han,et al.  Orthogonal Laplacianfaces for Face Recognition , 2006, IEEE Transactions on Image Processing.

[22]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[23]  H. R. Martin,et al.  Application of statistical moments to bearing failure detection , 1995 .

[24]  Jin Chen,et al.  Feature extraction of rolling bearing’s early weak fault based on EEMD and tunable Q-factor wavelet transform , 2014 .

[25]  Jianshe Kang,et al.  Feature Extraction for Bearing Prognostics Based on Continuous Hidden Markov Model , 2014 .

[26]  Tao Liu,et al.  Singular spectrum analysis and continuous hidden Markov model for rolling element bearing fault diagnosis , 2015 .