Samenvatting
In recent years, machine learning (ML) based digital twins (DTs) have seen widespread application in the anomaly detection domain. A search-based literature survey revealed that the majority of the case studies focus on large-scale systems (i.e., nuclear power plant, aerospace, and power grid) producing extensive data. Our work aims to investigate the performance of this technology in smaller-scale systems that generate less data. In this case study, we developed a ML-based DT of the mobility system, the omni wheels, of the Turtle soccer robots. The DT is capable of analyzing historical data collected from the physical robots and differentiating between damaged and undamaged wheels. Our experiments suggest that ML-based DT of small-scale systems is indeed capable of achieving relatively accurate results for anomaly detection use-cases.
Originele taal-2 | Engels |
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Titel | 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) |
Uitgeverij | Institute of Electrical and Electronics Engineers |
Pagina's | 179-182 |
Aantal pagina's | 4 |
ISBN van elektronische versie | 979-8-3503-4235-2 |
DOI's | |
Status | Gepubliceerd - 1 jan. 2024 |
Evenement | 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) - Durres, Albanië Duur: 6 sep. 2023 → 8 sep. 2023 |
Congres
Congres | 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) |
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Land/Regio | Albanië |
Stad | Durres |
Periode | 6/09/23 → 8/09/23 |