A Concept for Accelerating Long-Term Prototype Testing Using Anomaly Detection and Digital Twins

Vincent Nebel, Pia Goßrau-Lenau, Harshvardhan Agarwal, Dirk Werth

2025

Abstract

Developing mechanical components, especially complex assemblies like pumps, is a resource and time intensive process. Testing pump prototypes for long-term durability is critical to ensure error-free operation of the final product. Prototypes undergo material and operational tests to determine their expected lifespan, focusing on defects caused by material degradation and water contamination. Long-term tests, lasting months, are necessary to simulate real-world conditions, but limited test bench capacities create bottlenecks, restricting material experimentation. Moreover, monitoring the internal state of pumps during tests is challenging. Undetected defects can worsen or trigger secondary issues, complicating the root cause analysis, which provides valuable information for further product improvements. To address these challenges, a digital twin that integrates geometry and material data, simulations, and sensor measurements was developed. This twin is used as data source for machine learning based anomaly detection, allowing tests to stop sooner and preventing further damage when first signs of a defect are detected. A modular serverless architecture is used to host the model inference on the cloud, improving resource usage and scalability as well as reducing operational costs.

Paper Citation

in Harvard Style

Nebel V., Goßrau-Lenau P., Agarwal H. and Werth D. (2025). A Concept for Accelerating Long-Term Prototype Testing Using Anomaly Detection and Digital Twins. In Proceedings of the 15th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER; ISBN 978-989-758-747-4, SciTePress, pages 247-254. DOI: 10.5220/0013441500003950

in Bibtex Style

@conference{closer25,
author={Vincent Nebel and Pia Goßrau-Lenau and Harshvardhan Agarwal and Dirk Werth},
title={A Concept for Accelerating Long-Term Prototype Testing Using Anomaly Detection and Digital Twins},
booktitle={Proceedings of the 15th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER},
year={2025},
pages={247-254},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013441500003950},
isbn={978-989-758-747-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER
TI - A Concept for Accelerating Long-Term Prototype Testing Using Anomaly Detection and Digital Twins
SN - 978-989-758-747-4
AU - Nebel V.
AU - Goßrau-Lenau P.
AU - Agarwal H.
AU - Werth D.
PY - 2025
SP - 247
EP - 254
DO - 10.5220/0013441500003950
PB - SciTePress