Multi-source Semi-supervised Condition Constrained Domain Adaptation for Process Fault Classification

Abstract To address inconsistent feature distributions caused by multiple working conditions in industrial processes and the lack of fault type labels, this paper proposes a Multi-source semi-supervised conditional constraint domain adaptation (MSSCCDA) fault classification method. Data from multiple working conditions are divided into multiple source domains and a target domain. A Convolutional neural network (CNN) extracts features from the multi-source domain data, and a triplet loss method reduces the feature distance between the same categories in different source domains. A semi-supervised conditional constraint domain adaptation method is proposed, which combines adversarial domain adaptation with a limited amount of labeled target domain data to pre-train the feature extractor. During adversarial training, center loss regularization is introduced as a conditional constraint for class feature alignment. The process of optimizing the feature extractor involves jointly leveraging adversarial loss to reduce inter-domain discrepancies between the source and target domains and minimizing class differences within target domain. Experiments conducted on the Tennessee-Eisman (TE) process and industrial three-phase flow (TFF) show that the average accuracy improves to 93% and 93%, respectively. The effectiveness of the proposed method is further validated through a comparison with two traditional domain adaptation approaches and five multi-source domain adaptation techniques.