A reinforcement ensemble deep transfer learning network for rolling bearing fault diagnosis with Multi-source domains. (January 2022)

• Record Type: Journal Article
• Authors: Li, Xingqiu; Jiang, Hongkai; Xie, Min; Wang, Tongqing; Wang, Ruixin; Wu, Zhenghong
• Is Part Of: Advanced engineering informatics. Volume 51(2022)
• Publication Date: 2022-01
• Subjects: Rolling bearing -- Fault diagnosis -- Multi-source domains -- Reinforcement ensemble deep transfer network -- Unified metric; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2021.101480
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: Fault diagnosis with transfer learning has achieved great attention. However, existing methods mostly focused on single-source-single-target sceneries. In some cases, there may exist multiple source domains. Therefore, a reinforcement ensemble deep transfer learning network (REDTLN) is proposed for fault diagnosis with multi-source domains. Firstly, various new kernel maximum mean discrepancies (kMMDs) are used to construct multiple deep transfer learning networks (DTLNs) for single-source-single-target domain adaptation. The differences of kernel functions and source domains can help the DTLNs learn diverse transferable features. Secondly, a new unified metric is designed based on kMMD and diversity measures for unsupervised ensemble learning. Finally, using the unified metric as the reward, a reinforcement learning method is firstly explored to generate an effective combination rule for multi-domain-multi-model reinforcement ensemble. The proposed method is verified with experiment datasets, and the results empirically show its effectiveness and superiority compared with other methods.