Multi-scale deep intra-class transfer learning for bearing fault diagnosis

Xu Wang, Changqing Shen, Min Xia, Dong Wang, Jun Zhu and Zhongkui Zhu

Reliability Engineering and System Safety, 2020, vol. 202, issue C

Abstract: The tremendous success of deep learning in machine fault diagnosis is dependent on the hypothesis that training and test datasets are subordinated to the same distribution. This subordination is difficult to meet in practical scenarios of industrial applications. On the one hand, the working conditions of rotating machinery can change easily. On the other hand, vibration data and labels are difficult to obtain to train a specific model for each working condition. In this study, we solve these problems by constructing a novel deep transfer learning model called multi-scale deep intra-class adaptation network, which first uses the modified ResNet-50 to extract low-level features and then constructs a multiple scale feature learner to analyze these low-level features at multiple scales and obtain high-level features as input for the classifier. Pseudo labels are then computed to shorten the conditional distribution distance of vibration data collected under different working loads for intra-class adaptation. The proposed method is validated using two datasets to recognize the bearing normal state, the inner race, the ball and outer race faults, and their fault degrees under four different working loads. The high-precision diagnosis results of 24 transfer learning experiments reveal the reliability and generalizability of the constructed model.

Keywords: Fault diagnosis; Deep transfer learning; Multi-scale feature learner; Intra-class adaptation (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (41)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:202:y:2020:i:c:s0951832020305512

DOI: 10.1016/j.ress.2020.107050

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