Systematic review of predictive maintenance practices in the manufacturing sector
abdeldjalil benhanifia, Zied Ben Cheikh, Paulo Moura Oliveira, António Valente, José Lima
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. • A detailed review of predictive maintenance for the manufacturing industry. • Analysis of machine learning and IoT for real-time fault detection and prediction. • Key challenges include data gaps, low adoption, and ROI measurement issues. • Future research should optimize methods, costs, big data, and security.