Explainable Fault Diagnosis: Rule-Based Approach with Improved Discrete Gray Wolf Optimization Algorithm

The increasing complexity and automation of industrial processes, such as those found in chemical manufacturing, have made fault detection and diagnosis more critical than ever. Faults in such systems can lead to severe safety, environmental, and economic consequences. To address these challenges, this paper proposes a novel, explainable fault diagnosis framework that integrates a rule-based classification approach intelligently generated with an improved Discrete Gray Wolf Optimization (IDGWO) algorithm. The goal is to develop a model that not only achieves high accuracy in fault detection, but also provides transparency and interpretability in its decision-making process. The proposed method is applied to the Tennessee Eastman Process (TEP), a widely used benchmark in fault diagnosis research. The IDGWO algorithm is used to optimize the generation of classification rules, allowing for the extraction of a concise yet powerful rule set. Experimental evaluation using a real TEP dataset demonstrates the effectiveness of the approach, achieving an accuracy of 96.58% with only 9 concise rules, each containing approximately two terms. These rules offer clear insights into the underlying process behavior, enabling domain experts to better understand and trust the system’s decisions. Compared to the traditional techniques in both accuracy and interpretability, the proposed model offers superior performance while maintaining interpretability, thereby supporting safer and more trustworthy industrial systems.