2021/07/05 by Leporowski, Błażej, Tola, Daniella, Hansen, Casper +1 · 1 citation
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Robotics (cs.RO)
paper · doi:10.48550/arxiv.2107.01955
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.