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Sustainable manufacturing, maintenance policies, prognostics and health management: A literature review

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  • Vrignat, Pascal
  • Kratz, Frédéric
  • Avila, Manuel

Abstract

The increasing complexity of industrial processes, the continuing search for higher profits, and increasingly demanding production constraints call for the implementation of a proactive and sustainable maintenance policy. Adapting maintenance policies that integrate the concepts and obligations of so-called sustainable development is a real challenge for companies. This can concern proactive maintenance operations aimed at providing a balance in the social dimension as well as the environmental and economic dimensions. Such a policy requires the introduction of substantial upstream analysis and the establishment of tools to be able to validate process performance continuity. Consequently, and in an Industry 4.0 context, being able to anticipate a system breakdown based on the estimation of its degradation while proposing a time window for a maintenance intervention has become essential. This anticipation aims to avoid failure situations with particularly significant impacts. Approaches and methods for prognostics and health management provide many answers. This paper provides a full update on the various indicators and methods that may respond to the proposed theme. The presentation of knowledge-based methods and data analysis will guide the reader through the research process.

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  • Vrignat, Pascal & Kratz, Frédéric & Avila, Manuel, 2022. "Sustainable manufacturing, maintenance policies, prognostics and health management: A literature review," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    • Handle: RePEc:eee:reensy:v:218:y:2022:i:pa:s095183202100630x
    DOI: 10.1016/j.ress.2021.108140
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    9. Santos, Augusto César de Jesus & Cavalcante, Cristiano Alexandre Virginio & Ren, Junru & Wu, Shaomin, 2023. "A novel delay time modelling method for incorporating reuse actions in three-state single-component systems," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    10. Li, Yan-Fu & Wang, Huan & Sun, Muxia, 2024. "ChatGPT-like large-scale foundation models for prognostics and health management: A survey and roadmaps," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    11. Toyosi Ademujimi & Vittaldas Prabhu, 2024. "Model-Driven Bayesian Network Learning for Factory-Level Fault Diagnostics and Resilience," Sustainability, MDPI, vol. 16(2), pages 1-22, January.
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    16. Stana Vasić & Marko Orošnjak & Nebojša Brkljač & Vijoleta Vrhovac & Kristina Ristić, 2024. "Identification of Criteria for Enabling the Adoption of Sustainable Maintenance Practice: An Umbrella Review," Sustainability, MDPI, vol. 16(2), pages 1-35, January.
    17. Ding, Wanmeng & Li, Jimeng & Mao, Weilin & Meng, Zong & Shen, Zhongjie, 2023. "Rolling bearing remaining useful life prediction based on dilated causal convolutional DenseNet and an exponential model," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
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