Explainable fault detection and diagnosis based on an IDEOA as applied to an industrial process

Even with all measures approved by industrial sector specialists to avoid faults leading to major accidents, this field still suffers from some issues. Therefore, the safety and reliability of these industrial systems become necessary, leading to focus more on anticipating fault occurrence by giving fault detection and diagnosis a high priority. To solve this problem, a large set of reliable methods has been developed. Machine learning-based methods have gained significant importance as they have achieved promising results. However, the black-box nature of the generated fault detection models has restricted their investigation by users. Thus, explainable models aim to show features that influence the detection model decision. In this study, an Improved Discrete Equilibrium Optimizer Algorithm (IDEOA), which aims to solve different discrete optimization problems, was proposed to generate a rule-based fault detection model easily explainable by reading its classification rules. To this end, the Opposition-Based Learning (OBL) strategy is adopted in the IDEOA to avoid being stuck in local optima. A key contribution of this study is the novel application of the methodology to the Tennessee Eastman Process. The result of this study is a fault diagnosis model that consists of 16 rules, six of them belong to normal operating conditions and the rest reveal fault occurrence (F4). Then, an accuracy value is calculated to assess the effectiveness of our approach by contrasting it with other algorithms described in the literature. The findings indicate that the proposed approach outperforms other methods.