Health condition prediction of gears using a recurrent neural network approach

IEEE Transactions on Reliability

Volumn 59, Issue 4, 2010, Pages 700-705

  Tian, Zhigang ^a   Zuo, Ming J ^b  

^a Concordia University ^*  (Canada)

^b UNIVERSITY OF ALBERTA   (Canada)

Author keywords

Gearbox; health condition; prediction; recurrent neural network

Indexed keywords

COMPARATIVE STUDIES; CONDITION-BASED MAINTENANCE; EXPERIMENT DATA; EXPERIMENTAL SYSTEM; GEARBOX; HEALTH CONDITION; NEURAL NETWORK PREDICTION MODEL; PREDICTION; RECURRENT NEURAL NETWORK MODEL; RESEARCH TOPICS; VIBRATION DATA;

FORECASTING; HEALTH; MATHEMATICAL MODELS; RECURRENT NEURAL NETWORKS;

GEARS;

EID: 78649765886     PISSN: 00189529     EISSN: None     Source Type: Journal    
DOI: 10.1109/TR.2010.2083231     Document Type: Article

References (15)

1. A. K. S. Jardine, D. M. Lin, and D. Banjevic, "A review on machinery diagnostics and prognostics implementing condition-based maintenance," Mechanical Systems and Signal Processing, Vol. 20, no. 7, pp. 1483-1510, 2006.
2. S. K. Yang, "A condition-based failure-prediction and processing-scheme for preventive maintenance," IEEE Trans. Reliability, Vol. 52, no. 3, pp. 373-383, Sep. 2003.
3. N. Gebraeel, A. Elwany, and J. Pan, "Residual life predictions in the absence of prior degradation knowledge," IEEE Trans. Reliability, Vol. 58, no. 1, pp. 106-117, Mar. 2009.
4. N. Gebraeel and J. Pan, "Prognostic degradation models for computing and updating residual life distributions in a time-varying environment," IEEE Trans. Reliability, Vol. 57, no. 4, pp. 539-550, Dec. 2009.
5. F. Peysson, M. Ouladsine, and R. Outbib, "A generic prognostic methodology using damage trajectory models," IEEE Trans. Reliability, Vol. 58, no. 2, pp. 277-285, Jun. 2009.
6. D. J. Inman, C. R. Farrar, and V. Lopes, Jr., Damage Prognosis: For Aerospace, Civil and Mechanical Systems. New York: John Wiley and Sons, 2005.
7. R. Q. Huang, L. F. Xi, and X. L. Li, "Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods,"Mechanical Systems and Signal Processing, vol. 21, no. 1, pp. 193-207, 2007.
8. J. Lee, J. Ni, D. Djurdjanovic, H. Qiu, and H. Liao, "Intelligent prognostics tools and e-maintenance," Computers in Industry, Vol. 57, pp. 476-489, 2006.
9. N. Gebraeel and M. A. Lawley, "A neural network degradation model for computing and updating residual life distributions," IEEE Trans. Automation Science and Engi., Vol. 5, no. 1, pp. 154-163, 2008.
10. P. W. Tse and D. P. Atherton, "Prediction of machine deterioration using vibration based fault trends and recurrent neural networks," Journal of Vibration and Acoustics, Transactions of ASME, Vol. 121, pp. 355-362, 1999.
11. R. Rojas, Neural Networks: A System Introduction. Berlin, Germany: Springer, 1996.
12. J. T. Connor, R. D. Martin, and L. E. Atlas, "Recurrent neural networks and robust time series prediction," IEEE Trans. Neural Netw., Vol. 5, no. 2, pp. 240-254, 1994.
13. Z. Tian, M. J. Zuo, and H. Z. Huang, "Extended recurrent neural network for time series prediction," in Proceedings of the Sixth International Conference on Reliability, Maintainability & Safety, Xi'an, China, 2004, pp. 669-674.
14. X. F. Fan, H. Lin, and M. J. Zuo, Gearbox Lifetime Assessment Experiment - Experiment Design & Operation Guide University of Alberta, Oct. 2004, Technical report.
15. W. Kuo and M. J. Zuo, Optimal Reliability Modeling: Principles and Applications. Hoboken: John Wiley & Sons, Inc., 2003.