Abstract
Recently, several studies tried to develop fault identification models for rolling element bearing based on unsupervised learning techniques. However, an accurate intelligent fault diagnosis system is still a big challenge. In this study, a deep functional auto-encoders (DFAEs) model with SoftMax classifier was designed for valuable feature extraction from massive raw vibration signals. To maximize the unsupervised feature learning ability of the proposed model, various activation functions were applied in an effective methodology, these hidden activation functions enhance significantly the sparsity of the training data-set. The proposed method was validated using the raw vibration signals measured from the machine with different bearing conditions. The achieved results showed that the high-superiority of the proposed model comparing to standard deep learning and other traditional fault diagnosis methods in terms of classification accuracy even with massive input data sets.
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Acknowledgements
This research is supported by the National Natural Science Foundation of China (Grant No. 5157520, 51675204), the National Science and Technology Major Project of China (Grant No. 2018ZX04035002-002), and the Science Challenge Project of China (Grant No. TZ2018006-0102-01).
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Anas Hamid Aljemely received the B.S. degree in Mechanical Engineering in University of Technology, Baghdad, Iraq, in 2003. His Master’s degree is achieved in Mechanical Engineering in Huazhong University of Science and Technology (HUST) in 2016, Wuhan, China. He is currently a Ph.D. candidate of the School of Mechanical Science and Engineering, HUST. His research interests include signal processing analysis techniques, mechanical fault diagnosis, machine leaning algorithms and deep learning methods.
Jianping Xuan earned his Ph.D. degree in Mechanical Engineering from Huazhong University of Science and Technology (HUST) in 1999. After postdoctoral work ended, he joined the Mechanical Engineering faculty, HUST, in 2002. From February 2013 to February 2014, he was a visiting scientist in Department of Mechanical Engineering, Massachusetts Institute of Technology, USA. Currently, he is a Professor with the Department of Mechanical Engineering, HUST. His research interests include Digital Manufacturing for difficult-to-machine Materials and Structures; Big Data Based Intelligent Maintenance Systems; PHM for Structures, Machinery, CNC systems and Machine Tools. He has published more than 60 journal papers.
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Aljemely, A.H., Xuan, J., Jawad, F.K.J. et al. A novel unsupervised learning method for intelligent fault diagnosis of rolling element bearings based on deep functional auto-encoder. J Mech Sci Technol 34, 4367–4381 (2020). https://doi.org/10.1007/s12206-020-1002-x
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DOI: https://doi.org/10.1007/s12206-020-1002-x