A multi-source information transfer learning method with subdomain adaptation for cross-domain fault diagnosis

作者：

Highlights：

• A novel multi-source information transfer learning model is proposed.

• Multi-branch structure is used to align the feature spatial distributions of each source domain and target domain separately.

• Local maximum mean discrepancy (LMMD) is used to fine-grained local alignment of subdomain distributions.

• The effectiveness of the method is verified by examples.

摘要

•A novel multi-source information transfer learning model is proposed.•Multi-branch structure is used to align the feature spatial distributions of each source domain and target domain separately.•Local maximum mean discrepancy (LMMD) is used to fine-grained local alignment of subdomain distributions.•The effectiveness of the method is verified by examples.

论文关键词：Fault diagnosis,Multi-source information,Domain adaptation,Distribution difference

论文评审过程：Received 15 July 2021, Revised 14 February 2022, Accepted 16 February 2022, Available online 22 February 2022, Version of Record 8 March 2022.

论文官网地址：https://doi.org/10.1016/j.knosys.2022.108466