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Abstract Predicting failure in solids has broad applications including earthquake prediction which remains an unattainable goal. However, recent machine learning work shows that laboratory earthquakes can be predicted using micro-failure events and temporal evolution of fault zone elastic properties. Remarkably, these results come from purely data-driven models trained with large datasets. Such data are equivalent to centuries of fault motion rendering application to tectonic faulting unclear. In addition, the underlying physics of such predictions is poorly understood. Here, we address scalability using a novel Physics-Informed Neural Network (PINN). Our model encodes fault physics in the deep learning loss function using time-lapse ultrasonic data. PINN models outperform data-driven models and significantly improve transfer learning for small training datasets and conditions outside those used in training. Our work suggests that PINN offers a promising path for machine learning-based failure prediction and, ultimately for improving our understanding of earthquake physics and prediction.
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Arcing Fault Detection with Interpretable Learning Model Under the Integration of Renewable Energy
Under the trend of deeper renewable energy integration, active distribution networks are facing increasing uncertainty and security issues, among which the arcing fault detection (AFD) has baffled researchers for years. Existing machine learning based AFD methods are deficient in feature extraction and model interpretability. To overcome these limitations in learning algorithms, we have designed a way to translate the non-transparent machine learning prediction model into an implementable logic for AFD. Moreover, the AFD logic is tested under different fault scenarios and realistic renewable generation data, with the help of our self-developed AFD software. The performance from various tests shows that the interpretable prediction model has high accuracy, dependability, security and speed under the integration of renewable energy.
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- Award ID(s):
- 1810537
- PAR ID:
- 10189314
- Date Published:
- Journal Name:
- 2019 North American Power Symposium (NAPS)
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/NAPS46351.2019.8999972
