mlsanityData to 14 Oct 2026

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

arXiv 2401.07871 ↗First posted 15 Jan 2024cs.AI cs.HC cs.LGcited by 10

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Abstract (arXiv)

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.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 37 of its 192 references that are in the map. First posted on arXiv 15 Jan 2024.

Builds on

37 of 192 references.

References · 37

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25 more
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Cited by year

Citing papers per year: 2024: 4; 2025: 6.

10 papers.

  1. 2025Leveraging LLM Agents and Digital Twins for Fault Handling in Process Plantscited by 9
  2. 2024Interpretable Prognostics with Concept Bottleneck Modelscited by 6
  3. 2024A Survey of Transformer Enabled Time Series Synthesiscited by 5
  4. 2024Towards consistency of rule-based explainer and black box model -- fusion of rule induction and XAI-based feature importancecited by 3
  5. 2025MAnchors: Memorization-Based Acceleration of Anchors via Rule Reuse and Transformationcited by 2
  6. 2025Rashomon perspective for measuring uncertainty in the survival predictive maintenance modelscited by 2
  7. 2024Explainable Anomaly Detection: Counterfactual driven What-If Analysiscited by 1
  8. 2025Enhancing failure prediction in nuclear industry: Hybridization of knowledge- and data-driven techniquescited by 1
  9. 2025On the Tunability of Random Survival Forests Model for Predictive Maintenancecited by 0
  10. 2025Explainable AI guided unsupervised fault diagnostics for high-voltage circuit breakerscited by 0