In recent years, the fourth industrial revolution has attracted attention worldwide. Several concepts were born in conjunction with this new revolution, such as predictive maintenance. This study aims to investigate academic advances in failure prediction. The prediction of failures takes into account concepts as a predictive maintenance decision support system and a design support system. We focus on frameworks that use machine learning and reasoning for predictive maintenance in Industry 4.0. More specifically, we consider the challenges in the application of machine learning techniques and ontologies in the context of predictive maintenance. We conduct a systematic review of the literature (SLR) to analyze academic articles that were published online from 2015 until the beginning of June 2020. The screening process resulted in a final population of 38 studies of a total of 562 analyzed. We removed papers not directly related to predictive maintenance, machine learning, as well as researches classified as surveys or reviews. We discuss the proposals and results of these papers, considering three research questions. This article contributes to the field of predictive maintenance to highlight the challenges faced in the area, both for implementation and use-case. We conclude by pointing out that predictive maintenance is a hot topic in the context of Industry 4.0 but with several challenges to be better investigated in the area of machine learning and the application of reasoning.

近年来，第四次工业革命引起了全世界的关注。伴随这一新革命而诞生了一些概念，例如预测性维护。本研究旨在调查故障预测方面的学术进展。故障预测将概念作为预测性维护决策支持系统和设计支持系统考虑在内。我们专注于使用机器学习和推理进行工业4.0预测维护的框架。更具体地说，我们考虑在预测性维护的背景下应用机器学习技术和本体所面临的挑战。我们对文献（SLR）进行了系统的分析，以分析从2015年到2020年6月初在线发表的学术文章。筛选过程最终产生了38项研究，总共进行了562项分析。我们删除了与预测性维护，机器学习以及归类为调查或评论的研究没有直接关系的论文。我们考虑三个研究问题，讨论了这些论文的建议和结果。本文在预测性维护领域做出了贡献，突出了该领域在实施和用例方面面临的挑战。我们通过指出结论来指出，在工业4.0的背景下，预测性维护是一个热门话题，但是在机器学习和推理的应用领域尚需更好地研究一些挑战。以及归类为调查或评论的研究。我们考虑三个研究问题，讨论了这些论文的建议和结果。本文在预测性维护领域做出了贡献，突出了该领域在实施和用例方面面临的挑战。我们通过指出结论来指出，预测性维护在工业4.0的背景下是一个热门话题，但在机器学习和推理的应用领域仍存在一些需要更好研究的挑战。以及归类为调查或评论的研究。我们考虑三个研究问题，讨论了这些论文的建议和结果。本文在预测性维护领域做出了贡献，突出了该领域在实施和用例方面面临的挑战。我们通过指出结论来指出，预测性维护在工业4.0的背景下是一个热门话题，但在机器学习和推理的应用领域仍存在一些需要更好研究的挑战。