Table of Contents Author Guidelines Submit a Manuscript
Shock and Vibration
Volume 2018, Article ID 6024874, 12 pages
https://doi.org/10.1155/2018/6024874
Research Article

An Enhancement Deep Feature Extraction Method for Bearing Fault Diagnosis Based on Kernel Function and Autoencoder

School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China

Correspondence should be addressed to Fengtao Wang; nc.ude.tuld@tfgnaw

Received 22 November 2017; Accepted 17 January 2018; Published 27 February 2018

Academic Editor: Murat Inalpolat

Copyright © 2018 Fengtao Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. H. Jiang, C. Li, and H. Li, “An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis,” Mechanical Systems and Signal Processing, vol. 36, no. 2, pp. 225–239, 2013. View at Publisher · View at Google Scholar · View at Scopus
  2. Y. Lei, M. J. Zuo, Z. He, and Y. Zi, “A multidimensional hybrid intelligent method for gear fault diagnosis,” Expert Systems with Applications, vol. 37, no. 2, pp. 1419–1430, 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. M. D. Prieto, G. Cirrincione, A. G. Espinosa, J. A. Ortega, and H. Henao, “Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks,” IEEE Transactions on Industrial Electronics, vol. 60, no. 8, pp. 3398–3407, 2013. View at Publisher · View at Google Scholar · View at Scopus
  4. X. Jin, M. Zhao, T. W. S. Chow, and M. Pecht, “Motor bearing fault diagnosis using trace ratio linear discriminant analysis,” IEEE Transactions on Industrial Electronics, vol. 61, no. 5, pp. 2441–2451, 2014. View at Publisher · View at Google Scholar · View at Scopus
  5. 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. View at Publisher · View at Google Scholar · View at Scopus
  6. X. Zhang, B. Wang, and X. Chen, “Intelligent fault diagnosis of roller bearings with multivariable ensemble-based incremental support vector machine,” Knowledge-Based Systems, vol. 89, pp. 56–85, 2015. View at Publisher · View at Google Scholar · View at Scopus
  7. S. J. Dong, L. L. Chen, B. P. Tang, X. Y. Xu, Z. Y. Gao, and J. Liu, “Rotating machine fault diagnosis based on optimal morphological filter and local tangent space alignment,” Shock and Vibration, vol. 2015, Article ID 893504, 9 pages, 2015. View at Publisher · View at Google Scholar · View at Scopus
  8. W. Sun, S. Shao, R. Zhao, R. Yan, X. Zhang, and X. Chen, “A sparse auto-encoder-based deep neural network approach for induction motor faults classification,” Measurement, vol. 89, pp. 171–178, 2016. View at Publisher · View at Google Scholar · View at Scopus
  9. G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” American Association for the Advancement of Science: Science, vol. 313, no. 5786, pp. 504–507, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  10. 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 rotating machinery with massive data,” Mechanical Systems and Signal Processing, vol. 72-73, pp. 303–315, 2016. View at Publisher · View at Google Scholar · View at Scopus
  11. H. Shao, H. Jiang, F. Wang, and H. Zhao, “An enhancement deep feature fusion method for rotating machinery fault diagnosis,” Knowledge-Based Systems, vol. 119, pp. 200–220, 2017. View at Publisher · View at Google Scholar · View at Scopus
  12. Z. Chen and W. Li, “Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network,” IEEE Transactions on Instrumentation and Measurement, vol. 66, no. 7, pp. 1693–1702, 2017. View at Publisher · View at Google Scholar · View at Scopus
  13. L. Guo, H. Gao, H. Huang, X. He, and S. Li, “Multifeatures fusion and nonlinear dimension reduction for intelligent bearing condition monitoring,” Shock and Vibration, vol. 2016, Article ID 4632562, 10 pages, 2016. View at Publisher · View at Google Scholar · View at Scopus
  14. Z. Chen, S. Deng, X. Chen, C. Li, R.-V. Sanchez, and H. Qin, “Deep neural networks-based rolling bearing fault diagnosis,” Microelectronics Reliability, vol. 75, pp. 327–333, 2017. View at Publisher · View at Google Scholar · View at Scopus
  15. M. Ring and B. M. Eskofier, “An approximation of the Gaussian RBF kernel for efficient classification with SVMs,” Pattern Recognition Letters, vol. 84, pp. 107–103, 2016. View at Publisher · View at Google Scholar · View at Scopus
  16. X. Qi, Z. Yuan, and X. Han, “Diagnosis of misalignment faults by tacholess order tracking analysis and RBF networks,” Neurocomputing, vol. 169, pp. 439–448, 2015. View at Publisher · View at Google Scholar · View at Scopus
  17. F. Wang, X. Chen, B. Dun, B. Wang, D. Yan, and H. Zhu, “Rolling bearing reliability assessment via kernel principal component analysis and weibull proportional hazard model,” Shock and Vibration, vol. 2017, Article ID 6184190, 11 pages, 2017. View at Publisher · View at Google Scholar · View at Scopus
  18. X. Rui, Z. Sanyuan, and X. Lei, “Nonlinear process monitoring based on improved kernel ICA,” in Proceedings of the 2006 International Conference on Computational Intelligence and Security, ICCIAS 2006, pp. 1742–1746, China, October 2006. View at Publisher · View at Google Scholar · View at Scopus
  19. W.-A. Yang, M. Xiao, W. Zhou, Y. Guo, W. Liao, and G. Shen, “Trace ratio criterion-based kernel discriminant analysis for fault diagnosis of rolling element bearings using binary immune genetic algorithm,” Shock and Vibration, vol. 2016, Article ID 8631639, 15 pages, 2016. View at Publisher · View at Google Scholar · View at Scopus
  20. Y. Bengio, A. Courville, and P. Vincent, “Representation learning: a review and new perspectives,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1798–1828, 2013. View at Publisher · View at Google Scholar · View at Scopus
  21. Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle, “Greedy layer-wise training of deep networks,” in Proceedings of the 20th Annual Conference on Neural Information Processing Systems (NIPS '06), pp. 153–160, Cambridge, Mass, USA, December 2006. View at Scopus
  22. D. Erhan, Y. Bengio, A. Courville, P.-A. Manzagol, P. Vincent, and S. Bengio, “Why does unsupervised pre-training help deep learning?” Journal of Machine Learning Research, vol. 11, pp. 625–660, 2010. View at Google Scholar · View at MathSciNet
  23. Z. Xu, M. Dai, and D. Meng, “Fast and efficient strategies for model selection of Gaussian support vector machine,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 39, no. 5, pp. 1292–1307, 2009. View at Publisher · View at Google Scholar · View at Scopus
  24. H. Xiong, M. N. S. Swamy, and M. O. Ahmad, “Optimizing the kernel in the empirical feature space,” IEEE Transactions on Neural Networks and Learning Systems, vol. 16, no. 2, pp. 460–474, 2005. View at Publisher · View at Google Scholar · View at Scopus
  25. H. Shao, H. Jiang, H. Zhao, and F. Wang, “A novel deep autoencoder feature learning method for rotating machinery fault diagnosis,” Mechanical Systems and Signal Processing, vol. 95, pp. 187–204, 2017. View at Publisher · View at Google Scholar · View at Scopus
  26. S. Kim, Y. Choi, and M. Lee, “Deep learning with support vector data description,” Neurocomputing, vol. 165, pp. 111–117, 2015. View at Publisher · View at Google Scholar · View at Scopus