Industrial Processes Fault Diagnosis Method Based on Expert System-Guided Neural Network Decision-Space Sparsification

Abstract

Industrial fault diagnosis (FD) often faces challenges due to the scarcity of labeled data and the inability of rule-based systems to handle high-dimensional nonlinearities. This study proposes expert system-assisted neural networks (ES-Nets), a novel hybrid framework featuring ES-guided decision-space sparsification to bridge symbolic reasoning with neural networks (NNs). Unlike traditional data-driven models, this knowledge-preconditioned architecture embeds domain-specific logic into a comprehensive knowledge base before training. Specifically, the optimization process constrains the gradient descent trajectory to a knowledge-consistent subspace, effectively regularizing the parameter updates based on symbolic expert logic rather than purely on statistical gradients. Advanced embedding techniques pre-configure the model, enabling early operational performance and significantly reducing training requirements. Validation on the Tennessee Eastman (TE) process and a real-world petrochemical plant case demonstrates the framework’s superiority in accuracy and operational efficiency. This study provides an efficient and interpretable solution, facilitating effective human–machine collaboration in complex industrial environments.