Bearing Health Condition Prediction Using Deep Belief Network

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Guangquan Zhao Xiaoyong Liu Bin Zhang Guohui Zhang Guangxing Niu Cong Hu

Abstract

Bearings play a critical role in maintaining safety and reliability of rotating machinery. Bearings health condition prediction aims to prevent unexpected failures and minimize overall maintenance costs since it provides decision making information for condition-based maintenance. This paper proposes a Deep Belief Network (DBN)-based data-driven health condition prediction method for bearings. In this prediction method, a DBN is used as the predictor, which includes stacked RBMs and regression output. Our main contributions include development of a deep leaning-based data-driven prognosis solution that does not rely on explicit model equations and prognostic expertise, and providing comprehensive prediction results on five representative run-to-failure bearings. The IEEE PHM 2012 challenge dataset is used to demonstrate the effectiveness of the proposed method, and the results are compared with two existing methods. The results show that the proposed method has promising performance in terms of short-term health condition prediction and remaining useful life prediction for bearings.

How to Cite

Zhao, G., Liu, X., Zhang, B., Zhang, G., Niu, G., & Hu, C. (2017). Bearing Health Condition Prediction Using Deep Belief Network. Annual Conference of the PHM Society, 9(1). https://doi.org/10.36001/phmconf.2017.v9i1.2484
Abstract 64 | PDF Downloads 21

##plugins.themes.bootstrap3.article.details##

Keywords

bearings, Condition prediction, Deep Belief Network

References
Sloukia F., Aroussi M. E., Medromi H., & Wahbi M. (2013). Bearings prognostic using mixture of gaussians hidden markov model and support vector machine. 2013 ACS International Conference on Computer Systems and Applications, May 27-30, Ifrane, Morocco.
Li N., Lei Y, Liu Z, & Lin J. (2014). A particle filteringbased approach for remaining useful life predication of rolling element bearings. IEEE Conference on Prognostics and Health Management (pp. 1-8), June 22-25, Cheney, WA, USA. doi: 10.1109/ICPHM.2014.7036367
Liu J., Wang W., Ma F., Yang Y. B., & Yang C. S. (2012). A data-model-fusion prognostic framework for dynamic system state forecasting. Engineering
Applications of Artificial Intelligence, vol. 25, no. 4, pp. 814–823.
Lei Y., Li N., Gontarz S., Lin J. Radkowski S., & Dybala J. (2016). A Model-Based method for remaining useful life prediction of machinery. IEEE Transactions on Reliability, vol. 65, no.3, pp.1314-1326.
Dui H., Si S., Zuo M. J., & Sun S. (2015). Semi-Markov process-based integrated importance measure for multistate systems. IEEE Transactions on Reliability, vol. 64, no. 2, pp. 754−765.
Si X. S., Wang W., Chen M. Y., Hu C. H., & Zhou D. H. A degradation path-dependent approach for remaining useful life estimation with an exact and closed-form solution. European Journal of Operational Research, vol. 226, no. 1, pp. 53−66.
Yu J. (2013). A nonlinear probabilistic method and contribution analysis for machine condition monitoring. Mechanical Systems & Signal Processing, vol. 37, no. 1, pp. 293–314.
Huang R., Xi L., Li X., Liu C. R., Qiu H., & Lee J. (2007). Residual life predictions for ball bearings based on selforganizing map and back propagation neural network methods. Mechanical Systems and Signal Processing, vol. 21, no. 1, pp. 193-207.
Maio F. D., Tsui K. L., & Zio E. (2012). Combining Relevance Vector Machines and exponential regression for bearing residual life estimation. Mechanical Systems and Signal Processing, vol. 31, pp. 405-427.
Zhao F., Chen J., Guo L., &Li X. (2009). Neuro-fuzzy based condition prediction of bearing health. Journal of Vibration and Control, vol. 15, no. 7, pp. 1079-1091.
Deutsch J., & He D. (2016). Using Deep Learning Based Approaches for Bearing Remaining Useful Life Prediction. Annual Conference of the Prognostics and Health Management Society 2016, October 3-6, Denver, Colorado, USA.
Zhao G., Zhang G., Ge Q., & Liu X. (2016). Research advances in fault diagnosis and prognostic based on deep learning.2016 Prognostics and System Health Management Conference, October 19-21, Chengdu China.
Tamilselvan P., & Wang P. (2013). Failure diagnosis using deep belief learning based health state classification. Reliability Engineering & System Safety, vol. 115, pp. 124–135.
Lei Y., Jia F., Lin J., Xing S., & Ding S. (2016). An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data. IEEE Transactions on Industrial Electronics, vol.63, pp.3137–3147.
Kuremoto T., Kimura S., Kobayashi K., & Obayashi M. (2014). Time series forecasting using a deep belief network with restricted Boltzmann machines. Neuro Computing, vol. 137, pp 47-56.
Babu G. S., Zhao P., & Li X. L. (2016). Deep convolutional neural network based regression approach for estimation of remaining useful life. International Conference on Database Systems for Advanced Applications (pp. 214-228), April16-19, Dallas, Texas, USA.
Hinton G. E., & Salakhutdinov R. R. (2006). Reducing the dimensionality of data with neural networks. Science, vol. 313, no. 5786, pp. 504-507.doi: 10.1126/science.1127647
Hinton G. E., Osindero S., & Teh Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, vol. 18, no. 7, pp. 1527-1554.
Nectoux P., Gouriveau R., Medjaher K., Ramasso E., Morello B., Zerhouni N., & Varnier C. (2012). PRONOSTIA: An experimental platform for bearings accelerated degradation tests. IEEE International Conference on Prognostics and Health Management, Jun. 18–21, Denver, Colorado, USA.
Sutrisno E., Oh H., Vasan A. S. S., & Pecht M. (2012). Estimation of remaining useful life of ball bearings using data driven methodologies. IEEE International Conference on Prognostics and Health Management, June 18–21, Denver, Colorado, USA.
Section
Technical Papers

Most read articles by the same author(s)

1 2 > >>