Semi-Heterogeneous Graph-Perception Network With Gradient-Weighted Class Activation Mapping for Class-Incremental Industrial Fault Recognition and Root Cause Diagnosis.

Modern industrial systems often operate under complex dynamics and strict reliability constraints, demanding a timely and precise fault diagnosis with efficient root cause analysis to ensure operational safety and minimize downtime. However, the inherent uncertainties and complexities of industrial processes present significant challenges for conventional diagnostic approaches. Specifically, even minor anomalies can escalate into critical incidents, while process uncertainties frequently induce distribution shifts, leading to novel fault types that complicate fault detection and diagnosis. To address these challenges, this article proposes a novel industrial flow topology-induced semi-heterogeneous graph perception network (IFT-SHGPN) model for class-incremental fault diagnosis of complex industrial processes. By embedding the physical topology of industrial processes into a semi-heterogeneous graph perception network (SHGPN) and incorporating gradient-weighted class activation mapping (Grad-CAM), the proposed approach demonstrates strong capability in effective class-incremental fault recognition and interpretable root cause analysis. Rigorous experiments on the Tennessee Eastman process (TEP) and a multiphase flow facility process under various operation conditions showcase the superiority of IFT-SHGPN over existing methods. The proposed approach achieves high diagnostic accuracy for both historical and emerging fault categories while enabling efficient root cause identification with low computational overhead, making it particularly suitable for deployment in resource-constrained industrial environments.