Predictive Maintenance of Industrial Assets: A Fault Detection and Diagnosis Approach with Explainable AI

Predictive maintenance (PdM) helps organizations to reduce equipment downtime, optimize maintenance schedules, and enhance operational efficiency. By leveraging machine learning algorithms to predict when equipment failure will likely occur, maintenance teams can proactively schedule maintenance activities and prevent unexpected breakdowns. Fault detection and diagnosis are essential components of PdM. Fault detection involves analyzing sensor data collected from equipment to identify deviations from normal behavior. Diagnosis, however, involves identifying the root cause of a fault or failure. A dataset of an industrial asset is used to evaluate the proposed study. K-means clustering anomaly detection approach is employed. Implementing machine learning (ML)-based fault categorization approaches revealed that Random Forest had the best results. Significant progress has been made in fault detection and diagnosis using ML, but the degree of their explainability is significantly limited by the “black-box” character of some ML techniques. Less emphasis has been placed on explainable artificial intelligence (XAI) approaches in maintenance. Therefore, the XAI tools, SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) have been used to acknowledge the extent of the variables to analyze the influence of respective features. A stability metric has been included to improve the explanation’s overall quality. The findings of this paper suggest that the utilization of XAI can offer significant contributions in terms of insights and solutions for addressing critical maintenance issues.