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Shock and Vibration
Volume 2017, Article ID 3084197, 12 pages
https://doi.org/10.1155/2017/3084197
Research Article

Fault Diagnosis for Rotating Machinery Based on Convolutional Neural Network and Empirical Mode Decomposition

Department of Automation, School of Information Science and Technology, Tsinghua University, Beijing, China

Correspondence should be addressed to Yuan Xie; moc.anis@70yeix

Received 7 March 2017; Revised 30 June 2017; Accepted 11 July 2017; Published 20 August 2017

Academic Editor: Giosuè Boscato

Copyright © 2017 Yuan Xie and Tao Zhang. 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. Sriyananda and D. R. Towill, “Fault diagnosis using time domain measurements,” Radio and Electronic Engineer, vol. 43, no. 9, pp. 523–533, 1973. View at Publisher · View at Google Scholar · View at Scopus
  2. Q. Hu, S. Zhang, and S. Yang, “Variable condition bearing fault diagnosis based on time-domain and artificial intelligence,” Applied Mechanics and Materials, vol. 203, pp. 329–333, 2012. View at Publisher · View at Google Scholar · View at Scopus
  3. B. Sreejith, A. K. Verma, and A. Srividya, “Fault diagnosis of rolling element bearing using time-domain features and neural networks,” in Proceedings of the IEEE Region 10 Colloquium and 3rd International Conference on Industrial and Information Systems (ICIIS '08), pp. 1–6, Kharagpur, India, December 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. O. R. Seryasat, M. Aliyari Shoorehdeli, F. Honarvar, and A. Rahmani, “Multi-fault diagnosis of ball bearing based on features extracted from time-domain and multi-class support vector machine (MSVM),” in Proceedings of the 2010 IEEE International Conference on Systems, Man and Cybernetics, (SMC '10), pp. 4300–4303, Istanbul, Turkey, October 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. J.-B. Chang, T.-F. Li, and P.-F. Li, “The selection of time domain characteristic parameters of rotating machinery fault diagnosis,” in Proceedings of the 2010 International Conference on Logistics Systems and Intelligent Management (ICLSIM '10), pp. 619–623, Harbin, China, January 2010. View at Publisher · View at Google Scholar · View at Scopus
  6. X. Zhou and D. Luo, “Research of amplitude-frequency domain parameters analysis for condition detection and fault diagnosis,” Research Journal of Applied Sciences, Engineering and Technology, vol. 4, no. 19, pp. 3787–3790, 2012. View at Google Scholar · View at Scopus
  7. K. Mao and Y. Wu, “Fault diagnosis of rolling element bearing based on vibration frequency analysis,” in Proceedings of the 3rd International Conference on Measuring Technology and Mechatronics Automation, (CMTMA '11), pp. 198–201, Shangshai, China, January 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. J. Cao, L. Chen, J. Zhang, and W. Cao, “Fault diagnosis of complex system based on nonlinear frequency spectrum fusion,” Measurement: Journal of the International Measurement Confederation, vol. 46, no. 1, pp. 125–131, 2013. View at Publisher · View at Google Scholar · View at Scopus
  9. Z. Jiang, W. Jiao, and S. Meng, “Fault diagnosis method of time domain and time-frequency domain based on information fusion,” Applied Mechanics and Materials, vol. 300, pp. 635–639, 2013. View at Publisher · View at Google Scholar · View at Scopus
  10. M. Cao, H. Pan, and X. Chang, “Research on automatic fault diagnosis based on time-frequency characteristics and PCA-SVM,” in Proceedings of the 13th International Conference on Ubiquitous Robots and Ambient Intelligence, (URAI '16), pp. 593–598, Xi'an, China, August 2016. View at Publisher · View at Google Scholar · View at Scopus
  11. Z. T. Yao and H. X. Pan, “The engine fault diagnosis based on time domain and frequency domain,” Advanced Materials Research, vol. 936, pp. 2243–2246, 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. X.-W. Deng, P. Yang, J.-S. Ren, and Y.-W. Yang, “Rolling bearings time and frequency domain fault diagnosis method based on Kurtosis analysis,” in Proceedings of the 6th IEEE PES Asia-Pacific Power and Energy Engineering Conference, (APPEEC '14), Hong Kong, China, December 2014. View at Publisher · View at Google Scholar · View at Scopus
  13. Y. Yu and C. Junsheng, “A roller bearing fault diagnosis method based on EMD energy entropy and ANN,” Journal of Sound and Vibration, vol. 294, no. 1, pp. 269–277, 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. O. Janssens, V. Slavkovikj, B. Vervisch et al., “Convolutional neural network based fault detection for rotating machinery,” Journal of Sound and Vibration, vol. 377, pp. 331–345, 2016. View at Publisher · View at Google Scholar · View at Scopus
  15. Z. Chen, C. Li, and R.-V. Sanchez, “Gearbox fault identification and classification with convolutional neural networks,” Shock and Vibration, vol. 2015, Article ID 390134, 10 pages, 2015. View at Publisher · View at Google Scholar · View at Scopus
  16. X. Guo, L. Chen, and C. Shen, “Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis,” Measurement: Journal of the International Measurement Confederation, vol. 93, pp. 490–502, 2016. View at Publisher · View at Google Scholar · View at Scopus
  17. T. Ince, S. Kiranyaz, L. Eren, M. Askar, and M. Gabbouj, “Real-time motor fault detection by 1-D convolutional neural networks,” IEEE Transactions on Industrial Electronics, vol. 63, no. 11, pp. 7067–7075, 2016. View at Publisher · View at Google Scholar · View at Scopus
  18. L. Han, J. Hong, and D. Wang, “Fault diagnosis of aero-engine bearings based on wavelet package analysis,” Tuijin Jishu/Journal of Propulsion Technology, vol. 30, no. 3, pp. 328–341, 2009. View at Google Scholar · View at Scopus
  19. M. Deriche, “Bearing fault diagnosis using wavelet analysis,” in Proceedings of the 2005 1st International Conference on Computers, Communications and Signal Processing with Special Track on Biomedical Engineering, (CCSP '05), pp. 197–201, Kuala Lumpur, Malaysia, Malaysia, November 2005. View at Publisher · View at Google Scholar · View at Scopus
  20. C. Junsheng, Y. Dejie, and Y. Yu, “A fault diagnosis approach for roller bearings based on EMD method and AR model,” Mechanical Systems and Signal Processing, vol. 20, no. 2, pp. 350–362, 2006. View at Publisher · View at Google Scholar · View at Scopus
  21. G. E. Hinton, S. Osindero, and Y.-W. Teh, “A fast learning algorithm for deep belief nets,” Neural Computation, vol. 18, no. 7, pp. 1527–1554, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  22. A. J. M. Timmermans and A. A. Hulzebosch, “Computer vision system for on-line sorting of pot plants using an artificial neural network classifier,” Computers and Electronics in Agriculture, vol. 15, no. 1, pp. 41–55, 1996. View at Publisher · View at Google Scholar · View at Scopus
  23. Y. Yao and Z. Huang, “Bi-directional LSTM Recurrent Neural Network for Chinese Word Segmentation,” in Neural Information Processing, vol. 9950 of Lecture Notes in Computer Science, pp. 345–353, Springer, Cham, 2016. View at Publisher · View at Google Scholar
  24. Li. Deng, “A tutorial survey of architectures, algorithms, and applications for deep learning,” in Transactions on Signal and Information Processing, 2014. View at Google Scholar
  25. T. Tagawa, Y. Tadokoro, and T. Yairi, “Structured denoising autoencoder for fault detection and analysis,” ACML, 2014. View at Google Scholar
  26. M. Sakurada and T. Yairi, “Anomaly detection using autoencoders with nonlinear dimensionality reduction,” in Proceedings of the 2nd Workshop on Machine Learning for Sensory Data Analysis, (MLSDA '14), pp. 4–11, Gold Coast, Australia QLD, Australia. View at Publisher · View at Google Scholar · View at Scopus
  27. N. K. Verma, V. K. Gupta, M. Sharma, and R. K. Sevakula, “Intelligent condition based monitoring of rotating machines using sparse auto-encoders,” in Proceedings of the 2013 IEEE International Conference on Prognostics and Health Management, (PHM '13), Gaithersburg, MD, USA, June 2013. View at Publisher · View at Google Scholar · View at Scopus
  28. B. Yan and Q. Weidong, “Aero-engine sensor fault diagnosis based on stacked denoising autoencoders,” in Proceedings of the 35th Chinese Control Conference, (CCC '16), pp. 6542–6546, Chengdu, China, July 2016. View at Publisher · View at Google Scholar · View at Scopus
  29. C. Ciresan Dan, “Flexible, high performance convolutional neural networks for image classification,” in Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI '11), vol. 22, Barcelona, Catalonia, Spain, July 2011.
  30. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proceedings of the 26th Annual Conference on Neural Information Processing Systems (NIPS '12), pp. 1097–1105, Lake Tahoe, Nev, USA, December 2012. View at Scopus
  31. M. Matsugu, K. Mori, Y. Mitari, and Y. Kaneda, “Subject independent facial expression recognition with robust face detection using a convolutional neural network,” Neural Networks, vol. 16, no. 5, pp. 555–559, 2003. View at Publisher · View at Google Scholar · View at Scopus
  32. C. Szegedy, W. Liu, Y. Jia et al., “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '15), pp. 1–9, Boston, Mass, USA, June 2015. View at Publisher · View at Google Scholar
  33. O. Russakovsky and etal., “Imagenet large scale visual recognition challenge,” International Journal of Computer Vision, vol. 115, no. 3, pp. 211–252, 2015. View at Google Scholar
  34. A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and F.-F. Li, “Large-scale video classification with convolutional neural networks,” in Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition, (CVPR '14), pp. 1725–1732, Columbus, OH, USA, June 2014. View at Publisher · View at Google Scholar · View at Scopus
  35. E. Grefenstette, P. Blunsom, N. de Freitas, and K. M. Hermann, “A Deep Architecture for Semantic Parsing,” in Proceedings of the ACL 2014 Workshop on Semantic Parsing, pp. 22–27, Baltimore, MD, USA, June 2014. View at Publisher · View at Google Scholar
  36. Y. LeCun, “Deep learning & convolutional networks,” http://yann.lecun.com/exdb/lenet. View at Publisher · View at Google Scholar
  37. N. E. Huang, Z. Shen, S. R. Long et al., “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” The Royal Society of London. Proceedings. Series A. Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903–995, 1998. View at Publisher · View at Google Scholar · View at MathSciNet
  38. R. Srinivasan, R. Rengaswamy, and R. Miller, “A modified empirical mode decomposition (EMD) process for oscillation characterization in control loops,” Control Engineering Practice, vol. 15, no. 9, pp. 1135–1148, 2007. View at Publisher · View at Google Scholar · View at Scopus
  39. E. Ambikairajah, “Emerging features for speaker recognition,” in Proceedings of the 6th International Conference on Information, Communications and Signal Processing, (ICICS '07), Singapore, Singapore, December 2007. View at Publisher · View at Google Scholar · View at Scopus
  40. Y. B. Yang and K. C. Chang, “Extraction of bridge frequencies from the dynamic response of a passing vehicle enhanced by the EMD technique,” Journal of Sound and Vibration, vol. 322, no. 4-5, pp. 718–739, 2009. View at Publisher · View at Google Scholar · View at Scopus
  41. N. E. Huang, Z. Shen, and S. R. Long, “A new view of nonlinear water waves: the Hilbert spectrum,” Annual Review of Fluid Mechanics, vol. 31, pp. 417–457, 1999. View at Publisher · View at Google Scholar · View at MathSciNet