当在现实世界中付诸实践时,预测性维护为故障检测和预后提出了一系列挑战,这些挑战在使用受控实验数据进行验证的研究中经常被忽视。或数值模拟。出于这个原因,本研究旨在回顾最近的进展,在机械故障诊断和故障预测在制造业中使用机器学习方法。对于这项系统审查,我们搜索了WebofScience,ACM数字图书馆,科学直接,Wiley在线图书馆,和IEEEXplore在2015年1月至2021年10月之间。包括采用机器学习算法在制造设备中执行机械故障检测或故障预测的全长研究,并提供了从工业案例研究中获得的经验结果。除了不是用英语写的或发表在具有JCR影响因子的同行评审期刊以外的来源的研究,会议记录和书籍章节/节。在4549条记录中,选择了44项主要研究。在其中37项研究中,使用人工神经网络(n=12)进行故障诊断和预测,决策树方法(n=11),混合模型(n=8),或潜在变量模型(n=6),其中一项研究独立采用两种不同类型的技术。其余的研究采用了各种机器学习技术,从基于规则的模型到基于分区的算法,只有两项研究使用在线学习方法来解决这个问题。这些算法的主要优点包括高性能,揭示复杂非线性关系和计算效率的能力,而最重要的限制是在存在概念漂移的情况下模型性能的降低。这篇综述表明,尽管近年来在制造业进行的研究数量一直在增加,更多的研究是必要的,以解决现实世界的场景所带来的挑战。
When put into practice in the real world, predictive maintenance presents a set of challenges for fault detection and prognosis that are often overlooked in studies validated with data from controlled experiments, or numeric simulations. For this reason, this study aims to review the recent advancements in mechanical fault diagnosis and fault prognosis in the manufacturing industry using machine learning methods. For this systematic review, we searched Web of Science, ACM Digital Library, Science Direct, Wiley Online Library, and IEEE Xplore between January 2015 and October 2021. Full-length studies that employed machine learning algorithms to perform mechanical fault detection or fault prognosis in manufacturing equipment and presented empirical results obtained from industrial case-studies were included, except for studies not written in English or published in sources other than peer-reviewed journals with JCR Impact Factor, conference proceedings and book chapters/sections. Of 4549 records, 44 primary studies were selected. In 37 of those studies, fault diagnosis and prognosis were performed using artificial neural networks (n = 12), decision tree methods (n = 11), hybrid models (n = 8), or latent variable models (n = 6), with one of the studies employing two different types of techniques independently. The remaining studies employed a variety of machine learning techniques, ranging from rule-based models to partition-based algorithms, and only two studies approached the problem using online learning methods. The main advantages of these algorithms include high performance, the ability to uncover complex nonlinear relationships and computational efficiency, while the most important limitation is the reduction in model performance in the presence of concept drift. This review shows that, although the number of studies performed in the manufacturing industry has been increasing in recent years, additional research is necessary to address the challenges presented by real-world scenarios.