Learning features from vibration signals for induction motor fault diagnosis

International Symposium on Flexible Automation, ISFA 2016

Volumn , Issue , 2016, Pages 71-76

  Shao, Siyu ^a   Sun, Wenjun ^a   Wang, Peng ^b   Gao, Robert X ^b   Yan, Ruqiang ^a  

^a SOUTHEAST UNIVERSITY   (China)

^b Case Western Reserve University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATION; COMPUTER AIDED DIAGNOSIS; FAILURE ANALYSIS; FEATURE EXTRACTION; INDUCTION MOTORS; LEARNING ALGORITHMS;

ACTIVATION FUNCTIONS; CLASSIFICATION ACCURACY; DEEP BELIEF NETWORK (DBN); FEATURE EXTRACTION METHODS; INTEGRAL COMPONENTS; MANUFACTURING MACHINE; RESTRICTED BOLTZMANN MACHINE; WAVELET PACKET TRANSFORMS;

FAULT DETECTION;

EID: 85010504049     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISFA.2016.7790138     Document Type: Conference Paper

References (18)

1. F. Althobian and A. Ball, "An approach to fault diagnosis of reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks," Expert Systems with Applications, vol. 41, pp. 4113-4122, 2014.
2. J. Lin and L. Qu, "Feature extraction based on Morlet wavelet and its application for mechanical fault diagnosis," Journal of Sound and Vibration, vol. 234, pp. 135-148, 2000.
3. Y. Bengio, "Learning deep architectures for AI," Foundations and trends® in Machine Learning, vol. 2, pp. 1-127, 2009.
4. th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, 2013, pp. 8595-8598.
5. G.E. Hinton, "To recognize shapes, first learn to generate images," Progress in Brain Research, vol. 165, pp. 535-547, 2007.
6. P. Hamel and E. Douglas, "Learning features from music audio with deep belief networks," 14th International Society for Music Information Retrieval Conference, Curitiba, pp. 339-344, 2010.
7. Y. Liu, S. Zhou, and Q. Chen, "Discriminative deep belief networks for visual data classification," Pattern Recognition, vol. 44, pp. 2287-2296, 2011.
8. P. Tamilselvan and P. Wang, "Failure diagnosis using deep belief learning based health state classification," Reliability Engineering and System Safety, vol. 115, pp. 124-135, 2013.
9. J.W. Sun, R. Wyss, A. Steinecker, and P. Glocker, "Automated fault detection using deep belief networks for the quality inspection of electromotors," Technisches Messen, vol. 81, pp. 255-263, April 2014.
10. G.E. Hinton, S. Osindero, and Y.W. Teh, "A fast learning algorithm for deep belief nets," Neural Computation, vol. 18, pp. 1527-1554, 2006.
11. R. Salakhutdinov and G.E. Hinton, "Deep boltzmann machines," International Conference on Artificial Intelligence and Statistics, 2009, pp. 448-455.
12. G.E. Hinton and R.R. Salakhutdinov, "Reducing the dimensionality of data with neural networks," Science, vol. 313, pp. 504-507, 2006.
13. L. Van der. Maaten and G. Hinton, "Visualizing data using t-SNE," Journal of Machine Learning Research, vol. 9, pp. 2579-2605, 2008.
14. A. Mohamed, D. Yu, and L. Deng, "Investigation of full-sequence training of deep belief networks for speech recognition," 11th Annual Conference of the International Speech, Chiba, 2010, pp. 2846-2849.
15. A. Mohamed, T.N. Sainath, G. Dahl, B. Ramabhadran, G.E. Hinton, and M.A. Picheny, "Deep belief networks using discriminative features for phone recognition," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011, pp. 5060-5063.
16. G.E. Hinton, "A practical guide to training restricted boltzmann machines," Momentum, vol. 9, pp. 926, 2010.
17. Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle, "Greedy layer-wise training of deep networks," Advances in Neural Information Processing Systems, vol. 19, pp. 153, 2007.
18. G.E. Hinton, "Learning multiple layers of representation," Trends in Cognitive Sciences, vol. 11, pp. 428-434, 2007.