Human-in-the-Loop XAI for Predictive Maintenance: A Systematic Review of Interactive Systems and Their Effectiveness in Maintenance Decision-Making
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| Publicado en: | Electronics vol. 14, no. 17 (2025), p. 3384-3413 |
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| Autor principal: | |
| Otros Autores: | , |
| Publicado: |
MDPI AG
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2079-9292 | ||
| 024 | 7 | |a 10.3390/electronics14173384 |2 doi | |
| 035 | |a 3249684698 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231458 |2 nlm | ||
| 100 | 1 | |a Risqi, Amaliah Nuuraan |u Centre for E-Mobility and Clean Growth, Coventry University, Coventry CV1 5FB, UK; amaliahn@coventry.ac.uk | |
| 245 | 1 | |a Human-in-the-Loop XAI for Predictive Maintenance: A Systematic Review of Interactive Systems and Their Effectiveness in Maintenance Decision-Making | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Literature Review | ||
| 520 | 3 | |a 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. | |
| 653 | |a Industrial applications | ||
| 653 | |a Collaboration | ||
| 653 | |a System effectiveness | ||
| 653 | |a Literature reviews | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Explainable artificial intelligence | ||
| 653 | |a Downtime | ||
| 653 | |a Decision making | ||
| 653 | |a Predictive maintenance | ||
| 653 | |a Generative artificial intelligence | ||
| 653 | |a Interactive systems | ||
| 653 | |a Industry 4.0 | ||
| 700 | 1 | |a Tjahjono Benny |u Centre for E-Mobility and Clean Growth, Coventry University, Coventry CV1 5FB, UK; amaliahn@coventry.ac.uk | |
| 700 | 1 | |a Palade Vasile |u Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry CV1 5FB, UK; ab5839@coventry.ac.uk | |
| 773 | 0 | |t Electronics |g vol. 14, no. 17 (2025), p. 3384-3413 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3249684698/abstract/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3249684698/fulltextwithgraphics/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3249684698/fulltextPDF/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |