Condition-based maintenance using machine learning and role of interpretability: a review

This article aims to review the literature on condition-based maintenance (CBM) by analyzing various terms, applications, and challenges. CBM is a technique that monitors existing condition of an industrial asset determine what needs be performed. enlightens readers with research in using machine learning artificial intelligence techniques related literature. A bibliometric analysis performed data collected from Scopus database. The foundation accurate anomaly detection diagnosis. Several machine-learning approaches have produced excellent results for However, due black-box nature models, level their interpretability limited. provides insight into strategies, techniques, interpretable model-agnostic methods are being applied. It has been found significant work done towards based-maintenance learning, but explainable got less attention CBM. Based review, we contend can provide unique insights opportunities addressing critical difficulties leading more informed decision-making. put forward encouraging directions this area.