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.

中文翻译：

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预测性维护系统中的数据挖掘：分类和系统回顾

预测性维护是一个研究领域，其主要目标是优化维护的时间和类型，以在各种工业系统上执行。该目标涉及最大化受监控系统的可用时间并最小化维护中使用的资源数量。由于工业 4.0 范式中工业系统监控的进步，预测性维护目前正在经历一场革命。同样，人工智能和数据挖掘的进步允许处理大量数据以提供更准确和先进的预测模型。在这种情况下，许多参与者对预测性维护研究产生了兴趣，成为学术界和工业界融合的计算领域最活跃的研究领域之一。本文的目的是进行系统的文献综述，从数据挖掘的角度概述有关预测性维护的研究现状。该评论提出了第一个分类法，它暗示了在任何数据挖掘过程中为解决预测性维护问题而考虑的不同阶段，将预测性维护任务与主要数据挖掘任务联系起来以解决它们。最后，本文就数据挖掘在预测性维护中的应用潜力提出了重大挑战和未来研究方向。该评论提出了第一个分类法，它暗示了在任何数据挖掘过程中为解决预测性维护问题而考虑的不同阶段，将预测性维护任务与主要数据挖掘任务联系起来以解决它们。最后，本文就数据挖掘在预测性维护中的应用潜力提出了重大挑战和未来研究方向。该评论提出了第一个分类法，它暗示了在任何数据挖掘过程中为解决预测性维护问题而考虑的不同阶段，将预测性维护任务与主要数据挖掘任务联系起来以解决它们。最后，本文就数据挖掘在预测性维护中的应用潜力提出了重大挑战和未来研究方向。