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

Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving