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

TL;DR

The paper surveys explainable methods applied to predictive maintenance, organizing them under XAI and iML within a PRISMA-guided review. It categorizes approaches into model-agnostic, model-specific, and interpretable ML, and catalogs a wide set of techniques (e.g., SHAP, LIME, CAM, DIFFI, ARCANA) used to explain PdM models across anomaly detection, fault diagnosis, and prognosis. Key contributions include a synthesis of datasets, challenges in explanation evaluation, and a call for human-centered, audience-specific explanations and standardized metrics. The work underscores the importance of trust and interpretability in Industry 4.0/5.0 PdM and outlines concrete directions for future research and practice.

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.

Figures (11)

• Figure 1: Visualization of XAI Design Cycle
• Figure 2: Visualization of interpretable ML Design Cycle
• Figure 3: PRISMA Search
• Figure 4: Articles published per year in our inclusion results
• Figure 5: Google Search Trend for PdM, XAI, and iML from our article years