Adaptable and Generic Methods for Monitoring and Prognostics of Energy Assets
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https://orcid.org/0000-0003-0980-1544
Murat Yildirim
Abstract
Monitoring and prognostics of energy assets are crucial for maintaining their reliability and efficiency. Effective monitoring ensures that potential issues are identified early, preventing unexpected failures and optimizing maintenance schedules. However, several challenges complicate this process in real-world scenarios, including poor data quality, low-fidelity and sparse data, the influence of external environmental factors, and diverse operating conditions and asset types. These challenges highlight the need for adaptable and generic solutions that can handle variability and complexity across different energy systems. This Ph.D. project aims to address these challenges by developing scalable, data-driven approaches for monitoring and prognostics. By focusing on creating adaptable and generic frameworks, the research seeks to provide robust solutions for real-world monitoring and prognostic problems for energy assets.
How to Cite
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Prognostics, Autonomous Monitoring, Maintenance Planning, Energy Assets
Kotary, J., Fioretto, F., Van Hentenryck, P., & Wilder, B. (2021). End-to-end constrained optimization learning: A survey. arXiv preprint arXiv:2103.16378.
Nejjar, I.,Wang, Q., & Fink, O. (2023). Dare-gram: Unsupervised domain adaptation regression by aligning inverse gram matrices. In Proceedings of the ieee/cvf conference on computer vision and pattern recognition (pp. 11744–11754).
Sadana, U., Chenreddy, A., Delage, E., Forel, A., Frejinger, E., & Vidal, T. (2024). A survey of contextual optimization methods for decision-making under uncertainty. European Journal of Operational Research.
Sun, B., & Saenko, K. (2016). Deep coral: Correlation alignment for deep domain adaptation. In Computer vision–eccv 2016 workshops: Amsterdam, the netherlands, october 8-10 and 15-16, 2016, proceedings, part iii 14 (pp. 443–450).
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