It is important to minimize the unscheduled downtime of machines caused by outages of machine components in highly automated production lines. Considering machine tools such as, grinding machines, the bearing inside of spindles is one of the most critical components. In the last decade, research has increasingly focused on fault detection of bearings. In addition, the rise of machine learning concepts has also intensified interest in this area. However, up to date, there is no single one-fits-all solution for predictive maintenance of bearings. Most research so far has only looked at individual bearing types at a time.

This paper gives an overview of the most important approaches for bearing-fault analysis in grinding machines. There are two main parts of the analysis presented in this paper. The first part presents the classification of bearing faults, which includes the detection of unhealthy conditions, the position of the error (e.g. at the inner or at the outer ring of the bearing) and the severity, which detects the size of the fault. The second part presents the prediction of remaining useful life, which is important for estimating the productive use of a component before a potential failure, optimizing the replacement costs and minimizing downtime.

重要的是最大程度地减少高度自动化生产线中因机器组件故障而导致的计划外停机时间。考虑到诸如磨床之类的机床，主轴内部的轴承是最关键的组件之一。在过去的十年中，研究越来越集中在轴承的故障检测上。另外，机器学习概念的兴起也引起了人们对该领域的兴趣。但是，迄今为止，还没有一种万能的轴承预测维修解决方案。到目前为止，大多数研究一次只研究单个轴承类型。

本文概述了用于磨床轴承故障分析的最重要方法。本文介绍了分析的两个主要部分。第一部分介绍了轴承故障的分类，其中包括对不健康状况的检测，错误的位置（例如，轴承的内圈或外圈）和严重性，后者可检测故障的大小。第二部分介绍了剩余使用寿命的预测，这对于在潜在故障之前估算组件的生产用途，优化更换成本以及最大程度地减少停机时间非常重要。