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

ABSTRACT: 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.
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.
Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving
http://arxiv.org/abs/2107.01955v1