Smart diagnosis of journal bearing rotor systems: Unsupervised feature extraction scheme by deep learning

Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM

Volumn 2016-October, Issue , 2016, Pages 109-116

  Oh, Hyunseok ^a   Jeon, Byung Chul ^a   Jung, Joon Ha ^a   Youn, Byeng D ^a  

^a Seoul National University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

EXTRACTION; FAILURE (MECHANICAL); FEATURE EXTRACTION; JOURNAL BEARINGS; SYSTEMS ENGINEERING;

BEARING-ROTOR SYSTEM; CURRENT PRACTICES; FAULT CLASSIFICATION ACCURACY; PHYSICS OF FAILURES; REAL APPLICATIONS; RELEVANT FEATURES; TWO DIMENSIONAL IMAGES; VIBRATION SIGNAL;

DEEP LEARNING;

EID: 85030214553     PISSN: 23250178     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, pp. 1798-1828. doi:10.1109/tpami.2013.50
2. Gan, M., Wang, C., & Zhu, C. (2016). 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. doi:10.1016/j.ymssp.2015.11.014
3. Ha, J.M., Youn, B.D., Oh, H., Han, B., Jung, Y., & Park, J. (2016). Autocorrelation-based time synchronous averaging for condition monitoring of planetary gearboxes in wind turbines. Mechanical Systems and Signal Processing, vol. 70-71, pp. 161-175. doi:10.1016/j.ymssp.2015.09.040
4. Hinton, G.E. (1999). Produts of experts. In Proceedings of the Ninth International Conference on Artificial Neural Networks, September 7-10, Edinburgh, UK.
5. Hinton, G.E., Osindero, S., & Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, vol. 18, pp. 1527-1554. doi:10.1162/neco.2006.18.7.1527
6. Jeon, B.C., Jung, J.H., Youn, B.D., Kim, Y.-W., & Bae, Y.-C. (2015). Datum unit optimization for robustness of a journal bearing diagnosis system. International Journal of Precision Engineering and Manufacturing, vol. 16, 2411-2425. doi:10.1007/s12541-015-0311-y
7. Jia, F., Lei, Y., Lin, J., Zhou, X., & Lu, N. (2016). 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. doi:10.1016/j.ymssp.2015.10.025
8. Jung, J.H., Jeon, B.C., Youn, B.D., Kim, D., & Kim, Y. (2015) Omni-directional regeneration (ODR) of gap sensor signal for journal bearing system diagnosis, In Proceedings of the 7th Annual Conference of the Prognostics and Health Management Society, October 18-22. Coronado, CA
9. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning Nature, vol. 521, pp. 436-444. doi:10.1038/nature14539
10. Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014). Prognostics and health management design for rotary machinery systems-Reviews, methodology and applications. Mechanical Systems and Signal Processing, vol. 42, pp. 314-334. doi:10.1016/j.ymssp.2013.06.004
11. Oh, H., Han, B., McCluskey, P., Han, C., & Youn, B.D. (2015). Physics-of-failure, condition monitoring, and prognostics of insulated gate bipolar transistor modules: A review. IEEE Transactions on Power Electronics, vol. 30, pp. 2413-2426. doi:10.1109/tpel.2014.2346485
12. Sarkar, S., Lore, K., Sarkar, S., Ramanan, V., Chakravarthy, S., Phoha, S., & Ray, A. (2015). Early detection of combustion instability from hi-speed flame images via deep learning and symbolic time series analysis, In Proceedings of the 7th Annual Conference of the Prognostics and Health Management Society, October 18-22. Coronado, CA
13. Shao, H., Jiang, H., Zhang, X., & Niu, M. (2015). Rolling bearing fault diagnosis using an optimization deep belief network. Measurement Science and Technology, vol. 26, 115002. doi:10.1088/0957-0233/26/11/115002
14. Smolensky, P. (1986). Information processing in dynamical systems: foundations of harmony theory vol 1. Parallel distributed processing: explorations in the microstructure of cognition. MA, USA: MIT Press
15. Tamilselvan, P., & Wang, P. (2013). Failure diagnosis using deep belief learning based health state classification Reliability Engineering & System Safety, vol. 115, pp. 124-135. doi:10.1016/j.ress.2013.02.022
16. Yan, W., & Yu, L. (2015). On accurate and reliable anomaly detection for gas turbine combustors: a deep learning approach, In Proceedings of the 7th Annual Conference of the Prognostics and Health Management Society, October 18-22. Coronado, CA
17. Yang, B., & Kim, K.J. (2006). Application of Dempster-Shafer theory in fault diagnosis of induction motors using vibration and current signals. Mechanical Systems and Signal Processing, vol. 20, pp. 403-420.