In this paper, we propose a novel deep learning method for feature extraction in prognostics and health management applications. The proposed method is based on Extreme Learning Machines (ELM) and Auto-Encoders (AE), which have demonstrated very good performance and very short training time compared to other deep learning methods on several applications, including image recognition problems. The proposed approach is applied to vibration condition monitoring data to extract features from normal operation (i.e. fault free conditions) without any additional expert knowledge or prior information on the type of signals and the information content in the datasets. The approach demonstrates a better performance in terms of trendability and monotonicity compared to commonly applied feature extraction methods.
How to Cite
Bearing, deep learning, feature learning, Extreme Learning Machines, Stacked Auto-Encoders
Benkedjouh, T., Medjaher, K., Zerhouni, N., & Rechak, S. (2013). Remaining useful life estimation based on nonlinear feature reduction and support vector regression. Engineering Applications of Artificial Intelligence, 26(7), 1751–1760. http://doi.org/10.1016/j.engappai.2013.02.006
Bolón-Canedo, V., Sánchez-Maroño, N., & Alonso-Betanzos, A. (2015). Recent advances and emerging challenges of feature selection in the context of big data. Knowledge-Based Systems, 86, 33–45. http://doi.org/10.1016/j.knosys.2015.05.014
Cambria, E., Huang, G.-B., Kasun, L. L. C., Zhou, H., Vong, C. M., Lin, J., … Liu, J. (2013). Extreme Learning Machines [Trends & Controversies]. IEEE Intelligent Systems, 28(6), 30–59. http://doi.org/10.1109/MIS.2013.140
Emmanouilidis, C., Hunter, A., MacIntyre, J., & Cox, C. (1999). Selecting features in neurofuzzy modeling by multiobjective genetic algorithms. Artificial Neural Networks, 1999. ICANN 99. Ninth International Conference on (Conf. Publ. No. 470). http://doi.org/10.1049/cp:19991201
Fan, W., Cai, G., Zhu, Z. K., Shen, C., Huang, W., & Shang, L. (2015). Sparse representation of transients in wavelet basis and its application in gearbox fault feature extraction. Mechanical Systems and Signal Processing, 56, 230–245. http://doi.org/10.1016/j.ymssp.2014.10.016
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, 72, 92–104. http://doi.org/10.1016/j.ymssp.2015.11.014
Gowid, S., Dixon, R., & Ghani, S. (2015). A novel robust automated FFT-based segmentation and features selection algorithm for acoustic emission condition based monitoring systems. Applied Acoustics, 88, 66–74. http://doi.org/10.1016/j.apacoust.2014.08.007
Huang, G. Bin. (2015). What are Extreme Learning Machines? Filling the Gap Between Frank Rosenblatt’s Dream and John von Neumann’s Puzzle. Cognitive Computation, 7(3), 263–278. http://doi.org/10.1007/s12559-015-9333-0
Huang, G.-B., Zhou, H., Ding, X., & Zhang, R. (2012). Extreme Learning Machine for Regression and Multiclass Classification. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 42(2), 513–529.
Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2004). Extreme learning machine: a new learning scheme of feedforward neural networks. In Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on (Vol. 2, pp. 985–990 vol.2).
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, 72, 303–315. http://doi.org/10.1016/j.ymssp.2015.10.025
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, 42(1), 314–334. http://doi.org/10.1016/j.ymssp.2013.06.004
Lei, Y., & Zuo, M. J. (2009). Gear crack level identification based on weighted K nearest neighbor classification algorithm. Mechanical Systems and Signal Processing, 23(5), 1535–1547. http://doi.org/10.1016/j.ymssp.2009.01.009
Lemos, A., Caminhas, W., & Gomide, F. (2013). Adaptive fault detection and diagnosis using an evolving fuzzy classifier. Information Sciences, 220, 64–85. http://doi.org/10.1016/j.ins.2011.08.030
Mavromatidis, G., Acha, S., & Shah, N. (2013). Diagnostic tools of energy performance for supermarkets using Artificial Neural Network algorithms. Energy and Buildings, 62, 304–314. http://doi.org/10.1016/j.enbuild.2013.03.020
Medjaher, K., Tobon-Mejia, D. a, & Zerhouni, N. (2012). Remaining Useful Life Estimation of Critical Components With Application to Bearings. IEEE Transactions on Reliability, 61(2), 292–302. http://doi.org/10.1109/TR.2012.2194175
Ming, A. B., Zhang, W., Qin, Z. Y., & Chu, F. L. (2016). Fault feature extraction and enhancement of rolling element bearing in varying speed condition. Mechanical Systems and Signal Processing, 76, 367–379. http://doi.org/10.1016/j.ymssp.2016.02.021
Nectoux, P., Gouriveau, R., Medjaher, K., Ramasso, E., Chebel-Morello, B., Zerhouni, N., & Varnier, C. (2012). PRONOSTIA : An experimental platform for bearings accelerated degradation tests.
Pan, J., Chen, J., Zi, Y., Li, Y., & He, Z. (2016). Mono-component feature extraction for mechanical fault diagnosis using modified empirical wavelet transform via data-driven adaptive Fourier spectrum segment. Mechanical Systems and Signal Processing, 72, 160–183. http://doi.org/10.1016/j.ymssp.2015.10.017
Rai, A., & Upadhyay, S. H. (2016). A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings. Tribology International, 96, 289–306. http://doi.org/10.1016/j.triboint.2015.12.037
Randall, R. B., & Antoni, J. (2011). Rolling element bearing diagnostics-A tutorial. Mechanical Systems and Signal Processing, 25(2), 485–520. http://doi.org/10.1016/j.ymssp.2010.07.017
Selak, L., Butala, P., & Sluga, A. (2014). Condition monitoring and fault diagnostics for hydropower plants. Computers in Industry, 65(6), 924–936. http://doi.org/10.1016/j.compind.2014.02.006
Shao, M., Zhu, X.-J., Cao, H.-F., & Shen, H.-F. (2014). An artificial neural network ensemble method for fault diagnosis of proton exchange membrane fuel cell system. Energy, 67, 268–275. http://doi.org/10.1016/j.energy.2014.01.079
Tamilselvan, P., & Wang, P. (2013). Failure diagnosis using deep belief learning based health state classification. Reliability Engineering & System Safety, 115(0), 124–135. http://doi.org/http://dx.doi.org/10.1016/j.ress.2013.02.022
Yan, W., Qiu, H., & Iyer, N. (2008). Feature extraction for bearing prognostics and health management (phm)-a survey (preprint). DTIC Document.
Yan, W., & Yu, L. (2015). On Accurate and Reliable Anomaly Detection for Gas Turbine Combustors: A Deep Learning Approach. Proceedings of the Annual Conference of the.
Yang, Y., Liao, Y., Meng, G., & Lee, J. (2011). A hybrid feature selection scheme for unsupervised learning and its application in bearing fault diagnosis. Expert Systems with Applications, 38(9), 11311–11320. http://doi.org/http://dx.doi.org/10.1016/j.eswa.2011.02.181
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.