A Novel Multimode Fault Classification Method Based on Deep Learning

Journal of Control Science and Engineering

Volumn 2017, Issue , 2017, Pages

  Zhou, Funa ^a   Gao, Yulin ^a   Wen, Chenglin ^b  

^a HENAN UNIVERSITY   (China)

^b HANGZHOU DIANZI UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

BEARINGS (MACHINE PARTS); DEEP LEARNING; DEEP NEURAL NETWORKS; MACHINERY; ROLLER BEARINGS;

CLASSIFICATION METHODS; CLASSIFICATION MODELS; FAULT CLASSIFICATION; FAULT SEVERITIES; MACHINERY EQUIPMENTS; MODE PARTITION; PREDICTIVE MAINTENANCE; ROLLING BEARINGS;

CLASSIFICATION (OF INFORMATION);

EID: 85017191962     PISSN: 16875249     EISSN: 16875257     Source Type: Journal    
DOI: 10.1155/2017/3583610     Document Type: Article

References (35)

1. D.-H. Zhou, Y. Liu, and X. He, "Review on fault diagnosis techniques for closed-loop systems, " Acta Automatica Sinica, vol. 39, no. 11, pp. 1933-1943, 2013.
2. F. N. Zhou, J. H. Park, and Y. J. Liu, "Differential feature based hierarchical PCA fault detection method for dynamic fault, " Neurocomputing, vol. 202, pp. 27-35, 2016.
3. C. Li, R.-V. Sanchez, G. Zurita, M. Cerrada, D. Cabrera, and R. E. Vásquez, "Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis, " Neurocomputing, vol. 168, pp. 119-127, 2015.
4. X. He, Z. Wang, Y. Liu, and D. H. Zhou, "Least-squares fault detection and diagnosis for networked sensing systems using a direct state estimation approach, " IEEE Transactions on Industrial Informatics, vol. 9, no. 3, pp. 1670-1679, 2013.
5. D. Zhao, D. Shen, andY. Q. Wang, "Fault diagnosis andcompensation for two-dimensional discrete time systems with sensor faults and time-varying delays, " International Journal of Robust and Nonlinear Control, 2017.
6. H. Li and D. Y. Xiao, "Surver on data driven fault classification methods, " Control and Decision, vol. 26, no. 1, pp. 1-16, 2011.
7. R. M. An and Y. Gao, "Spacecraft fault classification based on hierarchical neural network, " Spacecraft Environment Engineering, vol. 30, no. 2, pp. 203-208, 2013.
8. F. N. Zhou, C. L. Wen, Y. B. Leng, and Z. G. Chen, "A datadriven fault propagation analysis method, " Journal of Chemical Industry and Engineering (China), vol. 61, no. 8, pp. 1993-2001, 2010.
9. H. Ji, X. He, and D. Zhou, "On the use of reconstruction-based contribution for fault diagnosis, " Journal of Process Control, vol. 40, pp. 24-34, 2016.
10. D. J. Yu, M. F. Chen, J. S. Cheng, and Y. Yang, "A fault classification approach for rotor systems based on empirical mode decompositionmethod and support vectormachines, " Proceedings of the Chinese Society for Electrical Engineering, vol. 26, no. 16, pp. 162-167, 2006.
11. M. Gan, C. Wang, and C. A. Zhu, "Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings, " Mechanical Systems and Signal Processing, vol. 72-73, pp. 92-104, 2016.
12. G. F. Bin, J. J. Gao, X. J. Li, and B. S. Dhillon, "Early fault diagnosis of rotating machinery based on wavelet packets-empirical mode decomposition feature extraction and neural network, " Mechanical Systems and Signal Processing, vol. 27, no. 1, pp. 696-711, 2012.
13. A. Widodo and B.-S. Yang, "Support vectormachine inmachine condition monitoring and fault diagnosis, " Mechanical Systems and Signal Processing, vol. 21, no. 6, pp. 2560-2574, 2007.
14. D. Zhao, Z. Lin, and Y. Wang, "Integrated state/disturbance observers for two-dimensional linear systems, " IET Control Theory & Applications, vol. 9, no. 9, pp. 1373-1383, 2015.
15. Q. Hu, Z. He, Z. Zhang, and Y. Zi, "Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble, "Mechanical Systems and Signal Processing, vol. 21, no. 2, pp. 688-705, 2007.
16. L. Y. Wang, W. G. Zhao, and Y. Liu, "Rolling bearing fault diagnosis based on wavelet packet-neural network characteristic entropy, " Advanced Materials Research, vol. 108-111, no. 1, pp. 1075-1079, 2010.
17. Y. Yang and W. Tang, "Study of remote bearing fault diagnosis based on BP Neural Network combination, " in Proceedings of the 7th International Conference onNatural Computation (ICNC '11), pp. 618-621, IEEE, Shanghai, China, July 2011.
18. L. Jiang, Q. Li, J. Cui, and J. Xi, "Rolling bearing fault diagnosis based on higher-order cumulants and BP neural network, " in Proceedings of the 27th Chinese Control and Decision Conference (CCDC '15), pp. 2664-2667, IEEE, Qingdao, China, May 2015.
19. T. Kuremoto, S. Kimura, K. Kobayashi, and M. Obayashi, "Time series forecasting using a deep belief network with restricted Boltzmann machines, " Neurocomputing, vol. 137, pp. 47-56, 2014.
20. S. M. Zhang, F. L. Wang, S. Tan, and S. Wang, "A fully automatic onine mode identiflcation method for multi-mode processes, " Acta Automatica Sinica, vol. 42, no. 1, pp. 60-80, 2016.
21. J. Schmidhuber, "Deep Learning in neural networks: an overview, " Neural Networks, vol. 61, pp. 85-117, 2015.
22. G. E. Hinton and R. R. Salakhutdinov, "Reducing the dimensionality of data with neural networks, " Science, vol. 313, no. 5786, pp. 504-507, 2006.
23. H. Ze, A. Senior, and M. Schuster, "Statistical parametric speech synthesis using deep neural networks, " in Proceedings of the 38th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '13), pp. 7962-7966, IEEE, Vancouver, Canada, May 2013.
24. B. Song, S. Tan, and H. B. Shi, "Key principal components with recursive local outlier factor for multimode chemical process monitoring, " Journal of Process Control, vol. 47, pp. 136-149, 2016.
25. L. Zhao, C. Zhao, and F. Gao, "Inter-batch-evolution-traced process monitoring based on inter-batch mode division for multiphase batch processes, " Chemometrics and Intelligent Laboratory Systems, vol. 138, pp. 178-192, 2014.
26. Y. Zhang, C. Wang, and R. Lu, "Modeling and monitoring of multimode process based on subspace separation, " Chemical Engineering Research andDesign, vol. 91, no. 5, pp. 831-842, 2013.
27. F.-N. Zhou, C.-L. Wen, T.-H. Tang, and Z.-G. Chen, "DCA based multiple faults diagnosis method, " Acta Automatica Sinica, vol. 35, no. 7, pp. 971-982, 2009.
28. P. Tamilselvan and P. Wang, "Failure diagnosis using deep belief learning based health state classification, " Reliability Engineering & System Safety, vol. 115, pp. 124-135, 2013.
29. R. Huang, C. Liu, G. Li, and J. Zhou, "Adaptive deep supervised autoencoder based image reconstruction for face recognition, " Mathematical Problems in Engineering, vol. 2016, Article ID 6795352, 14 pages, 2016.
30. H. Liu, L. Li, and J. Ma, "Rolling bearing fault diagnosis based on STFT-deep learning and sound signals, " Shock andVibration, vol. 2016, Article ID 6127479, 12 pages, 2016.
31. P. L. Wang and C. J. Xia, "Fault detection and self-learning identification based on PCA-PDBNs, " Chinese Journal of Scientific Instrument, vol. 36, no. 5, pp. 1147-1154, 2015.
32. R. Pang, Z. B. Yu, W. Y. Xiong, and H. Li, "Faults recognition of high-speed train bogie based on deep learning, " Journal of Railway Science and Engineering, vol. 12, no. 6, pp. 1283-1288, 2015.
33. C. Lu, Z. Y. Wang, W. L. Qin, and J. Ma, "Faultdiagnosis of rotary machinery components using a stacked denoising autoencoderbased health state identification, " Signal Processing, vol. 130, pp. 377-388, 2017.
34. F. Jia, Y. Lei, J. Lin, X. Zhou, and N. Lu, "Deep neural networks: a promising tool for fault characteristic mining and intelligent diagnosis of rotatingmachinery withmassive data, " Mechanical Systems and Signal Processing, vol. 72-73, pp. 303-315, 2016.
35. Bearing Data Centre, Case Western Reserve University, http://csegroups. case. edu/bearingdatacenter/home.