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Blazej Tadeusz Leporowski

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

Publikation: Bidrag til bog/antologi/rapport/proceedingKonferencebidrag i proceedingsForskningpeer review

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.

OriginalsprogEngelsk
TitelTowards Sustainable Customization : Bridging Smart Products and Manufacturing Systems - Proceedings of the 8th Changeable, Agile, Reconfigurable and Virtual Production Conference CARV 2021 and 10th World Mass Customization and Personalization Conference MCPC 2021
RedaktørerAnn-Louise Andersen, Rasmus Andersen, Thomas Ditlev Brunoe, Maria Stoettrup Schioenning Larsen, Kjeld Nielsen, Alessia Napoleone, Stefan Kjeldgaard
Antal sider9
ForlagSpringer
Udgivelsesår2022
Sider224-232
ISBN (trykt)978-3-030-90699-3
ISBN (Elektronisk)978-3-030-90700-6
DOI
StatusUdgivet - 2022
Begivenhed10th World Mass Customization
& Personalization Conference: Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems
- Aalborg, Danmark
Varighed: 1 nov. 20212 nov. 2021
https://carv2020.com/mcpc2020/

Konference

Konference10th World Mass Customization
& Personalization Conference
LandDanmark
ByAalborg
Periode01/11/202102/11/2021
Internetadresse
SerietitelLecture Notes in Mechanical Engineering
ISSN2195-4356

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