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Domain-adaptive intelligence for fault diagnosis based on deep transfer learning from scientific test rigs to industrial applications

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Abstract

With the accumulation of data, the intelligent fault diagnosis of rolling bearings has achieved fruitful results, but it is costly to acquire and label data for industrial application. A series of studies achieve the reuse of knowledge from bearings in scientific test rigs (BSTRs) to bearings in industrial applications (BIAs) via transfer learning. Nevertheless, most of the cases ignore a constraint: the industrial datasets suffers from class imbalance. In general, industrial datasets lack data samples of fault states. To this end, we propose a pseudo-categorized maximum mean discrepancy (PCMMD), and use it to drive the multi-input multi-output convolutional network (MIMOCN) to narrow the cross-domain distribution discrepancy of various categories in the deep feature space. Firstly, the domain-shared encoder and classifier are pre-trained based on the source-domain labeled dataset. Then, the labeled source-domain data and unlabeled target-domain data are jointly used to train the MIMOCN. The pseudo-labels enable the PCMMD to measure the intra-class cross-domain distribution discrepancy. The target-domain reconstruction object function improves the credibility of target-domain deep feature. The transfer cases from artificial damaged BSTRs to real damage BSTRs verify the stability of the proposed PCMMD. In the case of transfer learning from balanced BSTRs to BIAs lacking fault samples, the diagnosis accuracy of BIAs is higher than that of existing methods.

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Fig. 1

source domain and b feature vectors extracted from target domain

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source-domain input, target-domain input, source-domain classification output and target-domain reconstruction output

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source domain (a) and target domain (b)

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Acknowledgements

This research is supported financially by National Natural Science Foundation of China (Grant No. 61633001, No. 51875437, and No. 51605403) and Natural Science Foundation of Fujian Province, China (No. 2016J01012).

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Authors and Affiliations

  1. School of Aerospace Engineering, Xiamen University, Xiamen, 361005, China

    Xincheng Cao, Binqiang Chen & Nianyin Zeng

  2. School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

    Xincheng Cao, Yu Wang & Binqiang Chen

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  1. Xincheng Cao
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  2. Yu Wang
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Correspondence to Yu Wang or Binqiang Chen.

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Cao, X., Wang, Y., Chen, B. et al. Domain-adaptive intelligence for fault diagnosis based on deep transfer learning from scientific test rigs to industrial applications. Neural Comput & Applic 33, 4483–4499 (2021). https://doi.org/10.1007/s00521-020-05275-x

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