  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.
