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

Jeetesh Sharma (), Murari Lal Mittal () and Gunjan Soni ()
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Jeetesh Sharma: Malaviya National Institute of Technology
Murari Lal Mittal: Malaviya National Institute of Technology
Gunjan Soni: Malaviya National Institute of Technology

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 4, No 3, 1345-1360

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

Keywords: Condition-based maintenance; Machine learning; Anomaly detection; Explainable artificial intelligence; Interpretability; Model-agnostic methods (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13198-022-01843-7

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