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

TL;DR

This paper presents a new dataset and demonstrates machine learning models for detecting faults in automated screwdriving, improving fault detection without manual fault modeling.

Contribution

It introduces a comprehensive dataset and applies ML models to automate fault detection in screwdriving, advancing data-driven manufacturing diagnostics.

Findings

ML models successfully detect faults in screwdriving operations

The dataset enables benchmarking of fault detection methods

Automated fault detection reduces manual effort and improves reliability

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.