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

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.

Automated summary

The study introduces a method for intelligent fault diagnosis of rolling bearings in industry through transfer learning, which improves the accuracy of fault diagnosis by narrowing the cross-domain distribution discrepancy of various categories in the deep feature space.