Remaining Useful Life Estimation of Bearings Based on Nonlinear Dimensional Reduction Combined with Timing Signals
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32797
Remaining Useful Life Estimation of Bearings Based on Nonlinear Dimensional Reduction Combined with Timing Signals

Authors: Zhongmin Wang, Wudong Fan, Hengshan Zhang, Yimin Zhou


In data-driven prognostic methods, the prediction accuracy of the estimation for remaining useful life of bearings mainly depends on the performance of health indicators, which are usually fused some statistical features extracted from vibrating signals. However, the existing health indicators have the following two drawbacks: (1) The differnet ranges of the statistical features have the different contributions to construct the health indicators, the expert knowledge is required to extract the features. (2) When convolutional neural networks are utilized to tackle time-frequency features of signals, the time-series of signals are not considered. To overcome these drawbacks, in this study, the method combining convolutional neural network with gated recurrent unit is proposed to extract the time-frequency image features. The extracted features are utilized to construct health indicator and predict remaining useful life of bearings. First, original signals are converted into time-frequency images by using continuous wavelet transform so as to form the original feature sets. Second, with convolutional and pooling layers of convolutional neural networks, the most sensitive features of time-frequency images are selected from the original feature sets. Finally, these selected features are fed into the gated recurrent unit to construct the health indicator. The results state that the proposed method shows the enhance performance than the related studies which have used the same bearing dataset provided by PRONOSTIA.

Keywords: Continuous wavelet transform, convolution neural network, gated recurrent unit, health indicators, remaining useful life.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 701


[1] F. Jia, Y. Lei, J. Lin, X. Zhou, and N. Lu, “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, pp. 303–315, 2016.
[2] L. Jie, W. Wang, and F. Golnaraghi, “An enhanced diagnostic scheme for bearing condition monitoring,” IEEE Transactions on Instrumentation & Measurement, vol. 59, no. 2, pp. 309–321, 2010.
[3] A. Ghods and H. H. Lee, “Probabilistic frequency-domain discrete wavelet transform for better detection of bearing faults in induction motors,” Neurocomputing, vol. 188, pp. 206–216, 2016.
[4] Y. Lei, L. Jing, M. J. Zuo, and Z. He, “Condition monitoring and fault diagnosis of planetary gearboxes: A review,” Measurement, vol. 48, no. 1, pp. 292–305, 2014.
[5] W. Yi, G. Xu, Q. Zhang, L. Dan, and K. Jiang, “Rotating speed isolation and its application to rolling element bearing fault diagnosis under large speed variation conditions,” Journal of Sound & Vibration, vol. 348, no. Switzerland, pp. 381–396, 2015.
[6] D. Wang and C. Shen, “An equivalent cyclic energy indicator for bearing performance degradation assessment,” Journal of Vibration and Control, vol. 22, no. 10, pp. 2380–2388, 2016.
[7] M. Pecht and J. Gu, “Physics-of-failure-based prognostics for electronic products,” Transactions of the Institute of Measurement and Control, vol. 31, no. 3-4, pp. 309–322, 2009.
[8] F. O. Heimes, “Recurrent neural networks for remaining useful life estimation,” in Prognostics and Health Management, 2008. PHM 2008. International Conference on. IEEE, 2008, pp. 1–6.
[9] Y. Qian, R. Yan, and R. X. Gao, “A multi-time scale approach to remaining useful life prediction in rolling bearing,” Mechanical Systems & Signal Processing, vol. 83, pp. 549–567, 2017.
[10] K. Javed, “A robust & reliable data-driven prognostics approach based on extreme learning machine and fuzzy clustering.” Ph.D. dissertation, Universit´e de Franche-Comt´e, 2014.
[11] S. Hong, Z. Zhou, E. Zio, and K. Hong, “Condition assessment for the performance degradation of bearing based on a combinatorial feature extraction method,” Digital Signal Processing, vol. 27, no. 1, pp. 159–166, 2014.
[12] Y. Lei, N. Li, S. Gontarz, L. Jing, S. Radkowski, and J. Dybala, “A model-based method for remaining useful life prediction of machinery,” IEEE Transactions on Reliability, vol. 65, no. 3, pp. 1314–1326, 2016.
[13] G. Liang, N. Li, J. Feng, Y. Lei, and L. Jing, “A recurrent neural network based health indicator for remaining useful life prediction of bearings,” Neurocomputing, vol. 240, no. C, pp. 98–109, 2017.
[14] G. S. Babu, P. Zhao, and X. L. Li, “Deep convolutional neural network based regression approach for estimation of remaining useful life,” in International Conference on Database Systems for Advanced Applications, 2016.
[15] M. Zhao, B. Tang, and T. Qian, “Bearing remaining useful life estimation based on time frequency representation and supervised dimensionality reduction,” Measurement, vol. 86, pp. 41–55, 2016.
[16] W. Long, X. Li, G. Liang, and Y. Zhang, “A new convolutional neural network-based data-driven fault diagnosis method,” IEEE Transactions on Industrial Electronics, vol. 65, no. 7, pp. 5990–5998, 2018.
[17] Y. Meyer, “L es ondelettes: Algorithmes et applications. armand c olin, paris, 1992,” Engl. Transl. Wavelets, Algorithms and Applications, SIAM, Philadelphia, PA, 1993.
[18] J. Bouvrie, “Notes on convolutional neural networks,” 2006.
[19] R. Jozefowicz, W. Zaremba, and I. Sutskever, “An empirical exploration of recurrent network architectures,” in International Conference on International Conference on Machine Learning, 2015.
[20] K. Cho, B. V. Merrienboer, C. Gulcehre, F. Bougares, and Y. Bengio, “Learning phrase representations using rnn encoder-decoder for statistical machine translation,” Computer Science, 2014.
[21] X. Jin, S. Yi, Z. Que, W. Yu, and T. W. S. Chow, “Anomaly detection and fault prognosis for bearings,” IEEE Transactions on Instrumentation & Measurement, vol. 65, no. 9, pp. 2046–2054, 2016.
[22] D. Eberhard and E. Voges, “Digital single sideband detection for interferometric sensors,” in International Conference on Optical Fiber Sensors, 1984.
[23] P. Nectoux, R. Gouriveau, K. Medjaher, E. Ramasso, B. Chebel-Morello, N. Zerhouni, and C. Varnier, “Pronostia: An experimental platform for bearings accelerated degradation tests.” in IEEE International Conference on Prognostics and Health Management, PHM’12. IEEE Catalog Number: CPF12PHM-CDR, 2012, pp. 1–8.
[24] T. Benkedjouh, K. Medjaher, N. Zerhouni, and S. Rechak, “Remaining useful life estimation based on nonlinear feature reduction and support vector regression,” Engineering Applications of Artificial Intelligence, vol. 26, no. 7, pp. 1751–1760, 2013.
[25] Z. K. Peng and F. L. Chu, “Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography,” Mechanical Systems & Signal Processing, vol. 18, no. 2, pp. 199–221, 2004.
[26] P. Sarlin, “Self-organizing time map: An abstraction of temporal multivariate patterns,” Neurocomputing, vol. 99, no. 1, pp. 496–508, 2013.
[27] A. Z. Hinchi and M. Tkiouat, “Rolling element bearing remaining useful life estimation based on a convolutional long-short-term memory network,” Procedia Computer Science, vol. 127, pp. 123–132, 2018.
[28] E. Sutrisno, H. Oh, A. S. S. Vasan, and M. Pecht, “Estimation of remaining useful life of ball bearings using data driven methodologies,” in IEEE Conference on Prognostics & Health Management, 2012.