Twin data multimode collaborative transfer learning for bearing failure diagnosis

Abstract

To tackle the challenges of gathering and labeling data in practical engineering applications, a multimode collaborative transfer learning method is proposed to bridge the reality gap between labeled twin fault data and unlabeled real-world data. A bearing fault digital twin model is built to produce failure twin data of the test bearing under different health conditions. Three simulation source domains are constructed in accordance with the twin data's transferable modes. The iterative joint geometric-statistical alignment is used to perform the collaborative transfer of twin data's modes, which suppresses the negative transfer brought on by insufficient transferable information and the large inter-domain discrepancy. The fuzzy integral decision fusion is optimized and used to automatically label the real-world samples, which increases the cross-domain category alignment feasibility. The experimental results of extensive diagnosis tasks verified that the proposed method significantly outperforms the state-of-the-art domain adaption methods and can achieve an average classification accuracy of more than 91% in the bearing fault diagnosis with the support of fault twin data alone, without the supervision and guidance of historical labeling data.