Data mining in predictive maintenance systems: A taxonomy and systematic review. (22nd June 2022) - Esteban, Aurora; Zafra, Amelia; Ventura, Sebastián
Abstract: Predictive maintenance is a field of study whose main objective is to optimize the timing and type of maintenance to perform on various industrial systems. This aim involves maximizing the availability time of the monitored system and minimizing the number of resources used in maintenance. Predictive maintenance is currently undergoing a revolution thanks to advances in industrial systems monitoring within the Industry 4.0 paradigm. Likewise, advances in artificial intelligence and data mining allow the processing of a great amount of data to provide more accurate and advanced predictive models. In this context, many actors have become interested in predictive maintenance research, becoming one of the most active areas of research in computing, where academia and industry converge. The objective of this paper is to conduct a systematic literature review that provides an overview of the current state of research concerning predictive maintenance from a data mining perspective. The review presents a first taxonomy that implies different phases considered in any data mining process to solve a predictive maintenance problem, relating the predictive maintenance tasks with the main data mining tasks to solve them. Finally, the paper presents significant challenges and future research directions in terms of the potential of data mining applied to predictive maintenance. This article is categorized under: Application Areas > Industry Specific Applications Technologies > Internet of Things Abstract : Predictive Maintenance from a Data Mining perspective: this review analyzes the most significant predictive maintenance (PdM) contributions in recent years from Data Mining (DM) perspective. An exhaustive study is carried out to determine the most used DM techniques for solving each specific PdM problem. A specific taxonomy is proposed that summarizes the main techniques in three main steps of the DM process: data acquisition, data preprocessing and model building. Moreover, DM tasks are related with the different PdM problems . Finally, the future trends considering the growth of the last few years in PdM from a DM perspective are also analyzed.