Abstract
Aiming at the limitation of early fault warning and the diagnosis of aero-engine main bearing when there are only normal operation data, a rolling bearing fault evolution state indicator based on deep convolutional neural network (CNN) and wavelet analysis was proposed. To be specific, firstly, the wavelet band envelope method was adopted to identify the early fault evolution process, and the feature distance between the degraded data and the normal ones was extracted by using deep CNN to develop the evolution state indicator. Then, the evolution stages were divided by using unsupervised clustering method. Finally, the remaining useful life (RUL) was predicted based on particle filter (PF). Three different groups of whole life cycle data of rolling bearings under various working conditions were used to prove the feasibility of the indicator. The results show that the wavelet-CNN features of completely different fault data show similar evolution trends, and the normalization of warning threshold can be realized based on the train labels. In conclusion, the results are of great significance for the early fault evolution monitoring, condition evaluation and remaining useful life prediction of rolling bearings without the absence of fault samples under actual aeroengine operation.
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This research is sponsored by National Science and Technology Major Project (J2019-IV-004-0071), National Natural Science Foundation of China (52272436).
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Xiyang Liu is a Ph.D. candidate in the College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, P. R. China. Her current research interests include deep learning and pattern recognition, and their applications in bearing fault diagnosis of aero engine.
Guo Chen received a Ph.D. degree in the School of Mechanical Engineering from the Southwest Jiaotong University, Chengdu, P. R. China, in 2000. Now he works at the College of General Aviation and Flight, Nanjing University of Aeronautics and Astronautics, Nanjing, P. R. China. His current research interests include the whole aero-engine vibration, rotor-bearing dynamics, rotating machine fault diagnosis, pattern recognition and machine learning, signal analysis and processing.
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Liu, X., Chen, G., Wei, X. et al. A rolling bearing fault evolution state indicator based on deep learning and its application. J Mech Sci Technol 37, 2755–2769 (2023). https://doi.org/10.1007/s12206-023-0504-8
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DOI: https://doi.org/10.1007/s12206-023-0504-8