Two-dimensional adversarial domain adaptation graph contrastive learning for fault diagnosis of limited similar batch process

Abstract

Batch process systems can encounter various faults that affect safety differently. Identifying the types of faults is crucial, while new processes often lack sufficient labels for differentiation. To mitigate label scarcity, migrating labels from old, well-labeled processes appears to be a cost-effective solution. In this regard, this paper proposes a novel fault diagnosis approach based on graph contrastive learning, termed the Two-Dimensional Adversarial Domain Adaptation-based Graph Contrastive Learning Network (2D-ADGCL). First, a 2D sliding window is employed to capture nonlinear dynamics between samples, and Statistical Pattern Analysis (SPA) is used to construct new samples containing more comprehensive statistical information. Second, a multi-head attention mechanism enhances the views of the graph structure. This facilitates the construction of positive and negative sample pairs for each instance, thus enabling the model to learn broader feature diversity and improving classification performance. Third, adversarial domain adaptation techniques are utilized to learn domain-invariant features between the source and target domains, enabling the classification of target domain samples for fault diagnosis. Finally, the sensitivity and superiority of 2D-ADGCL are validated through results from two experiments.