A bearing fault and severity diagnostic technique using adaptive deep belief networks and Dempster–Shafer theory

An artificial intelligent bearing fault and hierarchical severity diagnosis framework is proposed in this study. The framework utilizes a combined deep belief networks (DBNs) and Dempster–Shafer (D-S) theory fault diagnosis scheme and adopts a two-stage approach in classifying (1) bearing fault conditions and (2) fault severities. The combined fault diagnostic scheme first employs two parameter-optimized DBNs to process the horizontal and vertical vibration data acquired from the bearing house of a test rig, where the parameters of the DBNs are optimized using a hybrid genetic algorithm and particle swarm optimization algorithm proposed in this study. The classification results from the two DBNs are fused further using the D-S theory to improve the diagnostic accuracy. The fault diagnosis scheme is used first to classify the bearing fault conditions in Stage 1 from a bulk dataset containing all bearing operation conditions under study. The same diagnosis scheme is applied once more to classify the hierarchical fault severities for each fault condition in Stage 2 using the pre-classified data from Stage 1. The effectiveness of the framework is then evaluated on a set of bearing condition monitoring data. A comparison study between the results obtained using the current method and those from existing published work is also presented in the article. It is shown that the accuracy for bearing fault and severity diagnosis can be substantially improved by using the current framework.

[1]  Hee-Jun Kang,et al.  A survey on Deep Learning based bearing fault diagnosis , 2019, Neurocomputing.

[2]  Michael A. Wulder,et al.  An approach using Dempster-Shafer theory to fuse spatial data and satellite image derived crown metrics for estimation of forest stand leading species , 2013, Inf. Fusion.

[3]  Ming J. Zuo,et al.  Atomic decomposition and sparse representation for complex signal analysis in machinery fault diagnosis: A review with examples , 2017 .

[4]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[5]  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.

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

[7]  Hojjat Adeli,et al.  Enhanced probabilistic neural network with local decision circles: A robust classifier , 2010, Integr. Comput. Aided Eng..

[8]  V. Sugumaran,et al.  Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines , 2015 .

[9]  Wen-Chung Shen,et al.  Low-complexity sinusoidal-assisted EMD (SAEMD) algorithms for solving mode-mixing problems in HHT , 2014, Digit. Signal Process..

[10]  Andrew D. Ball,et al.  An approach to fault diagnosis of reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks , 2014, Expert Syst. Appl..

[11]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[12]  Zhiwen Liu,et al.  Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings , 2013, Neurocomputing.

[13]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[14]  Ευάγγελος Ζέρβας,et al.  Multisensor data fusion for fire detection , 2015 .

[15]  Léon Bottou,et al.  Large-Scale Machine Learning with Stochastic Gradient Descent , 2010, COMPSTAT.

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

[17]  S. Shanmugavel,et al.  Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor networks , 2016, Swarm Evol. Comput..

[18]  Hojjat Adeli,et al.  A novel unsupervised deep learning model for global and local health condition assessment of structures , 2018 .

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

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

[21]  Abdel-Ouahab Boudraa,et al.  Dempster-Shafer's Basic Probability Assignment Based on fuzzy Membership Functions , 2009, Progress in Computer Vision and Image Analysis.

[22]  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 .

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

[24]  John J. Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities , 1999 .

[25]  I. Daubechies,et al.  Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool , 2011 .

[26]  Abdel-Ouahab Boudraa,et al.  Dempster-Shafer's Basic Probability Assignment Based on Fuzzy Membership Functions , 2004 .

[27]  Geoffrey E. Hinton.  Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.

[28]  Tian Ran Lin,et al.  An adaptive sensitive frequency band selection method for empirical wavelet transform and its application in bearing fault diagnosis , 2019, Measurement.

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

[30]  Yu Luo,et al.  Determining Basic Probability Assignment Based on the Improved Similarity Measures of Generalized Fuzzy Numbers , 2015, Int. J. Comput. Commun. Control.

[31]  Xiaohong Yuan,et al.  Engine fault diagnosis based on multi-sensor information fusion using Dempster-Shafer evidence theory , 2007, Inf. Fusion.

[32]  Haidong Shao,et al.  Rolling bearing fault diagnosis using an optimization deep belief network , 2015 .

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

[34]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[35]  Alireza Alfi,et al.  PSO with Adaptive Mutation and Inertia Weight and Its Application in Parameter Estimation of Dynamic Systems , 2011 .

[36]  Sanjay H Upadhyay,et al.  A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings , 2016 .

[37]  C. L. Giles,et al.  Dynamic recurrent neural networks: Theory and applications , 1994, IEEE Trans. Neural Networks Learn. Syst..

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

[39]  Pingfeng Wang,et al.  Failure diagnosis using deep belief learning based health state classification , 2013, Reliab. Eng. Syst. Saf..

[40]  Ying Cao,et al.  Application of time series analysis and PSO–SVM model in predicting the Bazimen landslide in the Three Gorges Reservoir, China , 2016 .

[41]  Pascal Bouvry,et al.  Particle swarm optimization: Hybridization perspectives and experimental illustrations , 2011, Appl. Math. Comput..

[42]  Naif Alajlan,et al.  Deep learning approach for active classification of electrocardiogram signals , 2016, Inf. Sci..

[43]  Geoffrey E. Hinton.  A Practical Guide to Training Restricted Boltzmann Machines , 2012, Neural Networks: Tricks of the Trade.