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

Trust is essential for life-critical maintenance decisions: As predictive maintenance systems are deployed in serious applications where failures could threaten human safety, operators need to understand why the system makes its recommendations—not just accept predictions blindly. Explainability directly addresses this trust gap.

Explainability doesn't require sacrificing performance: The paper indicates that XAI methods can be integrated into predictive maintenance systems while maintaining strong predictive accuracy, meaning organizations don't face a trade-off between having trustworthy, understandable systems and having effective ones.

Multiple explanation approaches exist for different needs: The survey categorizes various XAI methods applicable to predictive maintenance, suggesting that different stakeholders (engineers, operators, managers) may benefit from different types of explanations tailored to their specific needs and technical backgrounds.

The field is still developing solutions to key challenges: While XAI methods are being applied to predictive maintenance, the paper identifies current challenges and gaps, indicating that responsible implementation of these systems requires ongoing research and that organizations should be cautious about over-relying on immature approaches.

Systematic evaluation frameworks are needed: By following PRISMA guidelines for systematic review, the paper emphasizes the importance of rigorous, standardized methods for evaluating explainable predictive maintenance systems—a critical step for ensuring these tools are genuinely trustworthy rather than just appearing transparent.