Using Deep Learning Based Approaches for Bearing Remaining Useful Life Prediction



Jason Deutsch David He


Traditional data driven prognostics requires establishing explicit model equations and much prior knowledge about signal processing techniques and prognostic expertise, and therefore is limited in the age of big data. This paper presents a deep learning based approach for bearing remaining useful life (RUL) prediction with big data. This approach has the ability to automatically extract important features that can be used for RUL predictions. The presented approach is tested and validated using data collected from bearing run-to-failure tests and compared with existing PHM methods. The test results show the promising bearing RUL prediction performance of the deep learning based approach.

How to Cite

Deutsch, J., & He, D. (2016). Using Deep Learning Based Approaches for Bearing Remaining Useful Life Prediction. Annual Conference of the PHM Society, 8(1).
Abstract 516 | PDF Downloads 270



bearing RUL prediction, deep learning, restricted boltzman machine

Baraldi, P., Compare, M., Sauco, S., & Zio, E. (2013). Ensemble neural network-based particle filtering for prognostics. Mechanical Systems and Signal Processing, vol. 41, no. 1, pp. 288–300.
Baraldi, P., Mangili, F., & Zio, E. (2012). A kalman filterbased ensemble approach with application to turbine creep prognostics. IEEE Transactions Reliability, vol. 61, pp. 966 – 977.
Bechhoefer, E., Clark, S., & He, D. (2010). A state space model for vibration based prognostics. Proceedings of the 2010 Annual Conference of the Prognostics and Health Management Society, Portland, OR, October 10 – 16.
Bengio, Y., Courville, A. & Vincent, P. (2013). Representation learning: A review and new perspectives, IEEE Transactions on Pattern Analysis
and Machine Intelligence, vol. 35, no. 8, pp. 1798-1828.
Chen, C., Zhang, B., Vachtsevanos, G., & Orchard, M. (2011). Machine condition prediction based on adaptive neuro–fuzzy and high-order particle filtering. IEEE Transactions on Industrial Electronics, vol. 58, no. 9, pp. 4353–4364.
Chen, Z., Zeng, X., Li, W., & Liao, G (2016). Machine fault classification using deep belief network, 2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings, Taipei, Taiwan, pp. 1-6.
Daigle, M. J. and Goebel, K. (2013). Model-based prognostics with concurrent damage progression processes. IEEE Transactions on Systems, Man,
Cybernetics, vol. 43, no. 3, pp. 535–546.
Heimes, F. (2008). Recurrent neural networks for remaining useful life estimation. Proceedings of the 2008 IEEE International Conference of Prognostics and Health Management, Denver, CO, pp. 1–6.
Hinton, G. E, Osindero, S., & The, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural Computing, vol. 18, no. 7, pp.1527-1554.
Hossain, M., Rekabdar, B., Louis, S. J., & Dascalu, S. (2015). Forecasting the weather of Nevada: A deep learning approach. International Joint Conference on Neural Networks (IJCNN), vol., no., pp.1-6.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature.
Li, R., Ma, J., Panyala, A., & He, D. (2010). Hybrid ceramic bearing prognostics using particle filtering. Proceedings of the 2010 Conference of the Society for Machinery Failure Prevention Technology, Huntsville, AL, April 13 – 15, pp. 57 – 69.
Lim, P., Goh, C. K., Tan, K. C., & Dutta, P. (2014). Estimation of remaining useful life based on switching kalman filter neural network ensemble. Proceedings of the 2014 Annual Conference of the Prognostics and Health Management Society, Fort Worth, TX, pp. 2–9.
Lv, Y., Duan, Y., Kang, W. Li, Z., Fei-Yue Wang, F.-Y. (2015). Traffic flow prediction with big data: A deep learning approach. IEEE Transactions on Intelligent Transportation Systems, vol.16, no.2, pp.865-873.
Malhi, A., Yan, R., & Gao, R. X. (2011). Prognosis of defect propagation based on recurrent neural networks. IEEE Transactions on Instrument and Measurement, vol. 60, no. 3, pp. 703–711.
Oliveira, T. P., Barbar, J. S., Soares, A. S. (2014). Multilayer perceptron and stacked autoencoder for Internet traffic prediction. Network and Parallel
Computing, vol. 8707 of the series Lecture Notes in Computer Science, pp. 61-71.
Shao, H., Jiang, H., Zhang, X., & Niu, M. (2015). Rolling bearing fault diagnosis using an optimization deep belief network. Measurement Science and Technology, Volume 26, Number 11.
Tao, Y., Chen, H., & Qiu, C. (2014). Wind power prediction and pattern feature based on deep learning method. Power and Energy Engineering Conference (APPEEC), 2014 IEEE PES Asia-Pacific , vol., no., pp.1-4.
Vachtsevanos, G., Lewis, F. L., Roemer, M., Hess, A., & Wu, B. (2006). Intelligent fault diagnosis and prognosis for engineering system. Hoboken, NJ: John Wiley & Sons, Inc.
Technical Papers

Most read articles by the same author(s)

1 2 > >>