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Foto: Universität Paderborn

Tanja Tornede

 Tanja Tornede

Software Innovation Campus Paderborn (SICP)

Wissenschaftliche Mitarbeiterin

Intelligente Systeme und Maschinelles Lernen

Wissenschaftliche Mitarbeiterin

Zukunftsmeile 2
33102 Paderborn

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Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance

T. Tornede, A. Tornede, M.D. Wever, E. Hüllermeier, in: Proceedings of the Genetic and Evolutionary Computation Conference, 2021


Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions

M.W. Hoffmann, S. Wildermuth, R. Gitzel, A. Boyaci, J. Gebhardt, H. Kaul, I. Amihai, B. Forg, M. Suriyah, T. Leibfried, V. Stich, J. Hicking, M. Bremer, L. Kaminski, D. Beverungen, P. zur Heiden, T. Tornede, Sensors (2020)

<jats:p>The development of renewable energies and smart mobility has profoundly impacted the future of the distribution grid. An increasing bidirectional energy flow stresses the assets of the distribution grid, especially medium voltage switchgear. This calls for improved maintenance strategies to prevent critical failures. Predictive maintenance, a maintenance strategy relying on current condition data of assets, serves as a guideline. Novel sensors covering thermal, mechanical, and partial discharge aspects of switchgear, enable continuous condition monitoring of some of the most critical assets of the distribution grid. Combined with machine learning algorithms, the demands put on the distribution grid by the energy and mobility revolutions can be handled. In this paper, we review the current state-of-the-art of all aspects of condition monitoring for medium voltage switchgear. Furthermore, we present an approach to develop a predictive maintenance system based on novel sensors and machine learning. We show how the existing medium voltage grid infrastructure can adapt these new needs on an economic scale.</jats:p>

AutoML for Predictive Maintenance: One Tool to RUL Them All

T. Tornede, A. Tornede, M.D. Wever, F. Mohr, E. Hüllermeier, in: Proceedings of the ECMLPKDD 2020, 2020

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