Matthias Wüest

Projects

AI-based Health Prognostics for Battery Energy Storage Systems with Operational Condition Monitoring Data / Team member / ongoing

Publications

Written conference contributions, peer-reviewed

• Wüest, M., Plonczak, A. and Goren Huber, L. (2026) 'Detecting anomalies under data scarcity : data granularity tradeoffs in large battery packs', in 2026 IEEE Swiss Conference on Data Science and AI (SDS). IEEE, pp. 91–98. doi: 10.1109/SDS70563.2026.00020.
• Plonczak, A., Wüest, M. and Goren Huber, L. (2026) 'Physics-constrained deep learning for interpretable anomaly detection in large battery packs with limited monitoring granularity', in Do, P., Ezhilarasu, C., and Bregon, A. (eds) Proceedings of the European Conference of the PHM Society 2026. PHM Society. doi: 10.36001/phme.2026.v9i1.4929.
• Wüest, M. and Goren Huber, L. (2025) 'Fully unsupervised anomaly detection in industrial images with unknown data contamination', in 2025 IEEE Swiss Conference on Data Science (SDS). IEEE, pp. 40–47. doi: 10.1109/SDS66131.2025.00013.

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