Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities

TL;DR

This survey reviews current explainable AI methods in predictive maintenance, emphasizing their role in building trust, discussing challenges, and outlining future research directions in the field.

Contribution

It categorizes existing XAI methods applied to predictive maintenance and discusses challenges and future opportunities in explainable predictive maintenance.

Findings

XAI methods enhance trust in predictive maintenance systems.

Current challenges include balancing explainability with model performance.

Future research should focus on standardized evaluation of XAI in maintenance.

Abstract

Predictive maintenance is a well studied collection of techniques that aims to prolong the life of a mechanical system by using artificial intelligence and machine learning to predict the optimal time to perform maintenance. The methods allow maintainers of systems and hardware to reduce financial and time costs of upkeep. As these methods are adopted for more serious and potentially life-threatening applications, the human operators need trust the predictive system. This attracts the field of Explainable AI (XAI) to introduce explainability and interpretability into the predictive system. XAI brings methods to the field of predictive maintenance that can amplify trust in the users while maintaining well-performing systems. This survey on explainable predictive maintenance (XPM) discusses and presents the current methods of XAI as applied to predictive maintenance while following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. We categorize the different XPM methods into groups that follow the XAI literature. Additionally, we include current challenges and a discussion on future research directions in XPM.