2023
DOI: 10.48550/arxiv.2303.10954
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Uncertainty-aware deep learning for digital twin-driven monitoring: Application to fault detection in power lines

Abstract: Deep neural networks (DNNs) are often coupled with physics-based models or data-driven surrogate models to perform fault detection and health monitoring of systems in the low data regime. These models serve as digital twins to generate large quantities of data to train DNNs which would otherwise be difficult to obtain from the real-life system. However, such models can exhibit parametric uncertainty that propagates to the generated data. In addition, DNNs exhibit uncertainty in the parameters learnt during tra… Show more

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