Cross-Domain Bilateral Transfer Learning for Fault Diagnosis Under Incomplete Multisource Domains

Abstract

Recently, transfer learning (TL) approaches have been extensively applied in industrial cross-domain fault diagnosis, most of which depend on the consistency assumption of the source and target fault categories. In practice, it is common to utilize multiple source domains for transfer learning, but each of them may not include all fault categories in the target domain, which are referred to as incomplete multisource domains. For the challenge of fault diagnosis under incomplete multisource domains, a cross-domain bilateral transfer learning (CDBTL) method is proposed in this article. First, a cross-domain bilateral transfer strategy is developed, where the source and target domains are reconstructed from each other and their distribution differences are reduced by minimizing the reconstruction error to avoid negative transfer. Then, for the source domain with label information, CDBTL maximizes the between-class distance of different fault categories and minimizes the within-class distance of the same fault category to ensure the discriminative nature of its feature representation. Afterwards, the common projection matrix is learned through the mutual cooperation of projection matrices between different incomplete source domains and target domain to compensate for the missing fault categories in a single source domain. The key to discriminate CDBTL from many exiting TL algorithms is that it relaxes the restriction of consistent fault categories in the source and target domains, and skillfully integrates the knowledge of multiple incomplete source domains. Extensive experiments on Tennessee Eastman process demonstrate the superiority of CDBTL in solving cross-domain fault diagnosis problem, whose accuracy is averagely improved by 17.99% compared with eleven existing algorithms.Note to Practitioners—The changing industrial operating modes (domains) may result in different data distributions and fault categories between the historical mode (source domain) and current mode (target domain). Traditional machine learning methods usually fail to diagnose under the above domain and category inconsistencies. The key work in this article is to develop a cross-domain bilateral transfer learning (CDBTL) algorithm to realize cross-domain fault diagnosis under incomplete multisource domains. The proposed algorithm can avoid negative transfer while reducing inter-domain differences, and utilize the mutual cooperation of multiple source domains to fully cover the fault categories in the target domain. The constructed CDBTL model can be combined with various classifiers, and the learned classifiers can be directly applied to fault diagnosis in advanced manufacturing industry under incomplete multisource domains.

Topics & Concepts