A Multisource Domain Adaptation Network for Process Fault Diagnosis Under Different Working Conditions

Abstract

Transfer learning-based process fault diagnosis has received intensive attention from researchers. However, a practical scenario of process fault diagnosis (i.e., multisource domain adaptation) has not been well solved under various working conditions. It is challenging since distribution difference coexists between different source domains and across source and target domains. In this study, a novel transfer learning model, feature-level and class-level based multi-source domain adaptation (FC-MSDA) is proposed for process fault diagnosis under varying working conditions. A common feature extractor is proposed to learn both global and local features from process signals. A feature selection module is developed to reduce negative transfer caused by irrelevant information in multiple source domains. Domain specific feature generator is developed for each source-target domain pair to learn domain-specific features. Moreover, class-level distribution alignment loss is proposed for each domain pair to settle the negative transfer caused by inconsistent label space between domains from different working conditions of process. An information fusion strategy is performed to ensemble multiple predictions. The experimental results on three industrial cases demonstrate the effectiveness of FC-MSDA in process fault diagnosis (i.e., FC-MSDA obtains the average accuracy of 99.17% on five transfer tasks in three phase process).