Explainable artificial intelligence (XAI) enabled anomaly detection and fault classification of an industrial asset

Abstract

Abstract Predictive maintenance 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. Anomaly detection and fault classification are essential components of predictive maintenance. Anomaly detection involves analyzing sensor data collected from equipment to identify deviations from normal behavior. Fault classification, on the other hand, involves identifying the root cause of a fault or failure. A dataset of an industrial asset is used to evaluate the proposed study. Four distinct data-driven anomaly detection methodologies were employed after the pre-processing of the data, with the deep learning-based autoencoder producing the best results of all the techniques. Implementing machine learning-based fault categorization approaches revealed that Random Forest had the best results. Bayesian optimization and sequential model-based hyperparameter optimization technique is used for greater accuracy and optimized hyperparameters. Significant progress has been made in anomaly detection and fault classification using machine learning, but the degree of their explainability is significantly limited by the ``black-box" character of some machine learning techniques. Less emphasis has been placed on explainable artificial intelligence (XAI) approaches in the domain of maintenance. Therefore, the XAI tools 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 article suggest that the utilization of eXplainable Artificial Intelligence (XAI) can offer significant contributions in terms of insights and solutions for addressing critical maintenance issues. As a result, decision-making processes can become more informed and effective.