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Human-in-the-Loop XAI for Predictive Maintenance: A Systematic Review of Interactive Systems and Their Effectiveness in Maintenance Decision-Making

Guardado en:
Udgivet i:Electronics vol. 14, no. 17 (2025), p. 3384-3413
Hovedforfatter: Risqi, Amaliah Nuuraan
Andre forfattere: Tjahjono Benny, Palade Vasile
Udgivet:
MDPI AG
Fag:
Industrial applications
Collaboration
System effectiveness
Literature reviews
Artificial intelligence
Explainable artificial intelligence
Downtime
Decision making
Predictive maintenance
Generative artificial intelligence
Interactive systems
Industry 4.0
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Resumen:Artificial intelligence (AI) plays a pivotal role in Industry 4.0, with predictive maintenance (PdM) emerging as a core application for improving operational efficiency by reducing unplanned downtime and extending asset life. Despite these advancements, the black-box nature of AI models remains a significant barrier to adoption, as industry stakeholders require systems that are both transparent and trustworthy. This study presents a systematic literature review examining how human-in-the-loop explainable AI (HITL-XAI) approaches can enhance the effectiveness and adoption of AI systems in PdM contexts. This review followed the PRISMA methodology, employing predefined search strings across Scopus, ProQuest, and EBSCO databases. Sixty-three peer-reviewed journal articles, published between 2019 and early 2025, were included in the final analysis. The selected studies span various domains, including industrial manufacturing, energy, and transportation, with findings synthesized through both descriptive and thematic analyses. A key gap identified is the limited empirical exploration of generative AI (GenAI) in improving the usability, interpretability, and trustworthiness of HITL-XAI systems in PdM applications. This review outlines actionable insights for integrating explainability and GenAI into existing rule-based PdM systems to support more adaptive and reliable maintenance strategies. Ultimately, the findings underscore the importance of designing HITL-XAI systems that not only demonstrate high model performance but are also effectively aligned with operational workflows and the cognitive needs of maintenance personnel.
ISSN:2079-9292
DOI:10.3390/electronics14173384
Fuente:Advanced Technologies & Aerospace Database