Systematic review of predictive maintenance practices in the manufacturing sector

Abdeldjalil Benhanifia , Zied Ben Cheikh , Paulo Moura Oliveira , Antonio Valente , José Lima
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引用次数: 0

Abstract

Predictive maintenance (PDM) is emerging as a strong transformative tool within Industry 4.0, enabling significant improvements in the sustainability and efficiency of manufacturing processes. This in-depth literature review, which follows the PRISMA 2020 framework, examines how PDM is being implemented in several areas of the manufacturing industry, focusing on how it is taking advantage of technological advances such as artificial intelligence (AI) and the Internet of Things (IoT). The presented in-depth evaluation of the technological principles, implementation methods, economic consequences, and operational improvements based on academic and industrial sources and new innovations is performed. According to the studies, integrating CDM can significantly increase machine uptime and reliability while reducing maintenance costs. In addition, the transition to PDM systems that use real-time data to predict faults and plan maintenance more accurately holds out promising prospects. However, there are still gaps in the overall methodologies for measuring the return on investment of PDM implementations, suggesting an essential research direction.
对制造部门的预测性维护实践进行系统审查
预测性维护(PDM)正在成为工业4.0中强大的变革性工具,可以显著提高制造过程的可持续性和效率。这篇深入的文献综述遵循PRISMA 2020框架,研究了PDM在制造业的几个领域是如何实施的,重点是它如何利用人工智能(AI)和物联网(IoT)等技术进步。对技术原理、实施方法、经济后果以及基于学术和工业来源和新创新的操作改进进行了深入评估。研究表明,集成CDM可以显著提高机器的正常运行时间和可靠性,同时降低维护成本。此外,向使用实时数据预测故障和更准确地计划维护的PDM系统的过渡具有良好的前景。然而,衡量PDM实施的投资回报的总体方法仍然存在差距,这表明了一个重要的研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
5.60
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0.00%
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