Bukowski Lech, Werbinska-Wojciechowska Sylwia
Faculty of Applied Sciences, WSB University, 1c Zygmunta Cieplaka Street, 41-300 Dabrowa Gornicza, Poland.
Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, Wyspianskiego 27, 50-370 Wroclaw, Poland.
Sensors (Basel). 2025 Aug 16;25(16):5100. doi: 10.3390/s25165100.
Industry 5.0 introduces a new paradigm where digital technologies support sustainable and human-centric industrial development. Within this context, resilience-based maintenance (RBM) emerges as a forward-looking maintenance strategy focused on system adaptability, fault tolerance, and recovery capacity under uncertainty. This article presents a systematic literature review (SLR) on RBM in the context of Maintenance 5.0. The review follows the PRISMA methodology and incorporates bibliometric and content-based analyses of selected publications. Key findings highlight the integration of AI methods, such as machine learning and digital twins, in enhancing system resilience. The results demonstrate how RBM aligns with the pillars of Industry 5.0, sustainability, and human-centricity, by reducing resource consumption and improving human-machine interaction. Research gaps are identified in AI explainability, sector-specific implementation, and ergonomic integration. The article concludes by outlining directions for developing Maintenance 5.0 as a strategic concept for resilient, intelligent, and inclusive industrial systems.
工业5.0引入了一种新的范式，即数字技术支持可持续和以人为本的工业发展。在此背景下，基于韧性的维护（RBM）作为一种前瞻性的维护策略出现，其专注于系统在不确定性下的适应性、容错能力和恢复能力。本文对维护5.0背景下的RBM进行了系统的文献综述（SLR）。该综述遵循PRISMA方法，并对所选出版物进行了文献计量和基于内容的分析。主要发现强调了人工智能方法（如机器学习和数字孪生）在增强系统韧性方面的整合。结果表明，RBM如何通过减少资源消耗和改善人机交互，与工业5.0、可持续性和以人为本的支柱保持一致。在人工智能可解释性、特定行业实施和人机工程学整合方面发现了研究差距。文章最后概述了将维护5.0发展成为弹性、智能和包容性工业系统战略概念的方向。
