Predictive maintenance is a well studied collection of techniques that aims to prolong the life of a mechanical system by using artificial intelligence and machine learning to predict the optimal time to perform maintenance. The methods allow maintainers of systems and hardware to reduce financial and time costs of upkeep. As these methods are adopted for more serious and potentially life-threatening applications, the human operators need trust the predictive system. This attracts the field of Explainable AI (XAI) to introduce explainability and interpretability into the predictive system. XAI brings methods to the field of predictive maintenance that can amplify trust in the users while maintaining well-performing systems. This survey on explainable predictive maintenance (XPM) discusses and presents the current methods of XAI as applied to predictive maintenance while following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. We categorize the different XPM methods into groups that follow the XAI literature. Additionally, we include current challenges and a discussion on future research directions in XPM.

翻译：预测性维护是一套经过充分研究的技术集合，旨在通过人工智能与机器学习预测最佳维护时机，从而延长机械系统的使用寿命。这些方法使系统和硬件维护人员能够降低维护所需的财务与时间成本。随着这些方法被应用于更严肃且可能危及生命的场景，人类操作员需要信任预测系统。这促使可解释人工智能（XAI）领域将可解释性和可诠释性引入预测系统。XAI为预测性维护领域带来了能在保持系统高性能的同时增强用户信任的方法。本综述遵循PRISMA 2020系统评价与荟萃分析优先报告条目指南，系统讨论了可解释预测性维护（XPM）中当前应用XAI方法的研究现状。我们遵循XAI文献的分类框架，将不同XPM方法划分为若干类别。此外，本文还包含了当前面临的挑战，并对XPM的未来研究方向进行了探讨。