Detecting faults in manufacturing applications can be difficult, especially if each fault model is to be engineered by hand. Data-driven approaches, using Machine Learning (ML) for detecting faults have recently gained increasing interest, where a ML model can be trained on a set of data from a manufacturing process. In this paper, we present a use case of using ML models for detecting faults during automated screwdriving operations, and introduce a new dataset containing fully monitored and registered data from a Universal Robot and OnRobot screwdriver during both normal and anomalous operations. We illustrate, with the use of two time-series ML models, how to detect faults in an automated screwdriving application.

翻译：数据驱动方法,利用机器学习(ML)来探测故障最近引起了越来越多的兴趣,在这个方法中,ML模型可以按照制造过程的一组数据进行培训。在本文中,我们提出了一个使用ML模型在自动螺旋操纵操作中探测故障的用案例,并引入了一个新的数据集,其中包括在正常和异常操作中从通用机器人和OnRobot螺丝起动器中充分监测和登记的数据。我们用两个时间序列ML模型来说明如何在自动螺旋操纵应用中探测故障。