Data-driven bearing fault identification using improved hidden Markov model and self-organizing map

Abstract Efficient condition monitoring and fault diagnosis of bearings are of great practical significance since bearings are key elements in most rotating manufacturing machineries. In this study, a condition monitoring index of bearings is developed based on self-organizing map (SOM) in order to detect incipient bearing faults quickly. It requires low computation cost and is robust to the change of load level and motor speed, hence is quite suitable for online condition monitoring of bearings. Furthermore, a novel hybrid algorithm combining diversified gradient descent (DGD) method and Bayesian model selection (BMS) called DGD-BMS for the optimization of discrete hidden Markov model (DHMM) parameters is formulated under a general Bayesian framework. The flexibility of the DGD-BMS consists in that the algorithm can increase the diversity of the searching paths generated for DHMM parameters so that the true underlying parameters are more likely to be found out. Thus it provides an effective way to avoid trapping in one local maximum. Both simulation and industrial case study are presented to validate the proposed approach. Results show that the monitoring index can detect incipient bearing faults efficiently with 100% accuracy even under varying load levels, and the DGD-BMS method achieves on average the classification rate of 99.58%. The proposed method exhibits excellent performance compared to the conventional gradient descent (GD) and Baum-Welch (BW) methods.

[1]  Huajing Fang,et al.  Increasing mapping based hidden Markov model for dynamic process monitoring and diagnosis , 2014, Expert Syst. Appl..

[2]  M.H. Hassoun,et al.  Fundamentals of Artificial Neural Networks , 1996, Proceedings of the IEEE.

[3]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[4]  Antonella Certa,et al.  ELECTRE TRI-based approach to the failure modes classification on the basis of risk parameters: An alternative to the risk priority number , 2017, Comput. Ind. Eng..

[5]  Jin Chen,et al.  Bearing fault recognition method based on neighbourhood component analysis and coupled hidden Markov model , 2016 .

[6]  Teuvo Kohonen,et al.  Self-organizing neural projections , 2006, Neural Networks.

[7]  A. Bowman,et al.  Applied smoothing techniques for data analysis : the kernel approach with S-plus illustrations , 1999 .

[8]  Philipp Slusallek,et al.  Introduction to real-time ray tracing , 2005, SIGGRAPH Courses.

[9]  Tony R. Martinez,et al.  The general inefficiency of batch training for gradient descent learning , 2003, Neural Networks.

[10]  H. Akaike.  A new look at the statistical model identification , 1974 .

[11]  David J. C. MacKay,et al.  Bayesian Interpolation , 1992, Neural Computation.

[12]  Chin-Hui Lee,et al.  Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains , 1994, IEEE Trans. Speech Audio Process..

[13]  Shahrul Kamaruddin,et al.  An overview of time-based and condition-based maintenance in industrial application , 2012, Comput. Ind. Eng..

[14]  Neri Merhav,et al.  The estimation of the model order in exponential families , 1989, IEEE Trans. Inf. Theory.

[15]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[16]  Bing Li,et al.  Fuzzy lattice classifier and its application to bearing fault diagnosis , 2012, Appl. Soft Comput..

[17]  Yongbin Liu,et al.  Time-Frequency Manifold as a Signature for Machine Health Diagnosis , 2012, IEEE Transactions on Instrumentation and Measurement.

[18]  Erkki Oja,et al.  Engineering applications of the self-organizing map , 1996, Proc. IEEE.

[19]  Jay Lee,et al.  Robust performance degradation assessment methods for enhanced rolling element bearing prognostics , 2003, Adv. Eng. Informatics.

[20]  Peter Congdon,et al.  Bayesian model choice based on Monte Carlo estimates of posterior model probabilities , 2006, Comput. Stat. Data Anal..

[21]  Jianbo Yu,et al.  Adaptive hidden Markov model-based online learning framework for bearing faulty detection and performance degradation monitoring , 2017 .

[22]  Jing Zhou,et al.  Automatic bearing fault diagnosis using particle swarm clustering and Hidden Markov Model , 2016, Eng. Appl. Artif. Intell..

[23]  Yongbo Li,et al.  A new rolling bearing fault diagnosis method based on multiscale permutation entropy and improved support vector machine based binary tree , 2016 .

[24]  Jin Chen,et al.  Detection and diagnosis of bearing faults using shift-invariant dictionary learning and hidden Markov model , 2016 .

[25]  Yujing Wang,et al.  Classification of fault location and the degree of performance degradation of a rolling bearing based on an improved hyper-sphere-structured multi-class support vector machine , 2012 .

[26]  Huaqing Wang,et al.  Intelligent diagnosis method for rolling element bearing faults using possibility theory and neural network , 2011, Comput. Ind. Eng..

[27]  T. Kohonen.  Self-organized formation of topographically correct feature maps , 1982 .

[28]  Minqiang Xu,et al.  A fault diagnosis scheme for rolling bearing based on local mean decomposition and improved multiscale fuzzy entropy , 2016 .

[29]  Frank Rosenblatt,et al.  PRINCIPLES OF NEURODYNAMICS. PERCEPTRONS AND THE THEORY OF BRAIN MECHANISMS , 1963 .

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

[31]  Huajing Fang,et al.  Diversified learning for continuous hidden Markov models with application to fault diagnosis , 2015, Expert Syst. Appl..

[32]  Amparo Alonso-Betanzos,et al.  Automatic bearing fault diagnosis based on one-class ν-SVM , 2013, Comput. Ind. Eng..

[33]  Mahmood R. Azimi-Sadjadi,et al.  Multi-aspect target discrimination using hidden Markov models and neural networks , 2005, IEEE Transactions on Neural Networks.

[34]  Orestes Llanes-Santiago,et al.  Optimizing kernel methods to reduce dimensionality in fault diagnosis of industrial systems , 2015, Comput. Ind. Eng..

[35]  Thomas R. Kurfess,et al.  Rolling element bearing diagnostics in run-to-failure lifetime testing , 2001 .

[36]  Jen-Tzung Chien,et al.  Predictive hidden Markov model selection for speech recognition , 2005, IEEE Transactions on Speech and Audio Processing.

[37]  Hu-Chen Liu,et al.  Fault diagnosis and cause analysis using fuzzy evidential reasoning approach and dynamic adaptive fuzzy Petri nets , 2013, Comput. Ind. Eng..

[38]  Chin-Hui Lee,et al.  Bayesian adaptive learning of the parameters of hidden Markov model for speech recognition , 1995, IEEE Trans. Speech Audio Process..

[39]  L. R. Rabiner,et al.  An introduction to the application of the theory of probabilistic functions of a Markov process to automatic speech recognition , 1983, The Bell System Technical Journal.

[40]  ChangKyoo Yoo,et al.  Statistical process monitoring with independent component analysis , 2004 .

[41]  Feng Liu,et al.  Sparse discriminant manifold projections for bearing fault diagnosis , 2017 .