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Physics-informed Lightweight Temporal Convolution Networks for Fault Prognostics Associated to Bearing Stiffness Degradation

Autor: Weikun Deng, Khanh T. P. Nguyen, Christian Gogu, Jérôme Morio, Kamal Medjaher

Publikováno v: PHM Society European Conference. 7:118-125

This paper proposes hybrid methods using physics-informed (PI) lightweight Temporal Convolution Neural Network (PITCN) for bearings’ remaining useful life (RUL) prediction under stiffness degradation. It includes three PI hybrid models: a) PI Featu

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::11feedbceaad239ee99d18cea13c99d3
https://doi.org/10.36001/phme.2022.v7i1.3365

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Physics Informed Self Supervised Learning For Fault Diagnostics and Prognostics in the Context of Sparse and Noisy Data

Autor: Weikun Deng, Khanh T. P. Nguyen, Kamal Medjaher

Publikováno v: PHM Society European Conference. 7:574-576

Sparse & noisy monitoring data leads to numerous challenges in prognostic and health management (PHM). Big data volume but poor quality with scarce healthy states information limits the performance of training machine learning (ML) and physics-based

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9b7b11786f92ed2246cdca9846660ffb
https://doi.org/10.36001/phme.2022.v7i1.3298

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