Physics-Informed Machine Learning for Robust Remaining Useful Life estimation of Power MOSFETs

Fassi, Youssof; Heiries, Vincent; Boutet, Jérôme; Boisseau, Sébastien

doi:10.1109/icphm61352.2024.10626501

2024 IEEE International Conference on Prognostics and Health Management (ICPHM) 2024

DOI: 10.1109/icphm61352.2024.10626501

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Physics-Informed Machine Learning for Robust Remaining Useful Life estimation of Power MOSFETs

Youssof Fassi,

Vincent Heiries,

Jérôme Boutet

et al.

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2024

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A Practical Prognostics Method Based on Stepwise Linear Approximation of a Nonlinear Degradation Model

2024

Applied Sciences

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Prognostics aims to predict the remaining useful life (RUL) of an in-service system based on its degradation data. Existing methods, such as artificial neural networks (ANNs) and their variations, often face challenges in real-world applications due to their complexity and the lack of sufficient data. In this paper, a practical prognostic method is proposed, based on the stepwise linear approximation of nonlinear degradation behavior, to simplify the prognostic process while significantly reducing computational costs and maintaining high accuracy. The proposed approach is validated using synthetic data generated at different noise levels, with 100 data sets tested at each level, and compared against a typical ANN method. The results demonstrate that the proposed method consistently outperforms the ANN in terms of accuracy and robustness, while remarkably reducing computational time by a factor of 50 to 60, making it a promising solution for real-world applications.

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A Practical Prognostics Method Based on Stepwise Linear Approximation of a Nonlinear Degradation Model

2024

Applied Sciences

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Physics-Informed Machine Learning for Robust Remaining Useful Life estimation of Power MOSFETs

Cited by 1 publication

References 17 publications

A Practical Prognostics Method Based on Stepwise Linear Approximation of a Nonlinear Degradation Model

A Practical Prognostics Method Based on Stepwise Linear Approximation of a Nonlinear Degradation Model

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