Abstract
This article begins by exploring the fundamental role of descriptive data mining in enhancing maintenance and reliability across various physical systems. It then provides an in-depth review on the use of association rules for enhancing maintenance and reliability in physical systems, with a strong emphasis on the crucial role of data mining in extracting patterns from large datasets to inform maintenance strategies, thereby reducing failures and costs. It meticulously analyzes the literature to identify industrial applications of association rules, data preparation for rule extraction, and the interpretation of performance in maintenance, while also pinpointing research gaps. Moreover, it highlights a significant surge in the literature within the energy sector and proposes a forward-looking research agenda tailored to the Industry 4.0 era, focusing on integrating climatic data with production processes and applying data mining in the maintenance of the infrastructure of the smart city. This agenda aims to advance predictive maintenance strategies, thereby enhancing the efficiency, safety, and sustainability of manufacturing systems amid their transition towards smarter, more sustainable operations.
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