Physics-informed machine learning in prognostics and health management: State of the art and challenges

•Systematic bibliometric analysis of PIML in PHM.•Novel perspectives for PIML from the “Informed knowledge forms” and “Informed methods”.•Taxonomy of PIML approaches in PHM.•Highlight remaining challenges and future perspectives based on this review. Prognostics and health management (PHM) plays a c...

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Vydáno v:	Applied mathematical modelling Ročník 124; s. 325 - 352
Hlavní autoři:	DENG, Weikun, NGUYEN, Khanh T.P., MEDJAHER, Kamal, GOGU, Christian, MORIO, Jérôme
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Inc 01.12.2023 Elsevier
Témata:	Engineering Sciences Knowledge Materials Physics-constraint learning Physics-embedded algorithm structure Physics-informed input space Physics-informed machine learning Prognostics and health management ANN CNN DNN GNN NMAE Physics-informed machine learning SVM Knowledge PDE RNN PHM PBM MSE Physics-embedded algorithm structure LSTM NODE RUL RMSE Prognostics and health management PCA FCN MAE ROM Physics-constraint learning CRA KSVD NMSE VAE Physics-informed input space PIML DRM
ISSN:	0307-904X, 1872-8480
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Shrnutí:	•Systematic bibliometric analysis of PIML in PHM.•Novel perspectives for PIML from the “Informed knowledge forms” and “Informed methods”.•Taxonomy of PIML approaches in PHM.•Highlight remaining challenges and future perspectives based on this review. Prognostics and health management (PHM) plays a constructive role in the equipment’s entire life health service. It has long benefited from intensive research into physics modeling and machine learning methods. However, in practice, the existing solutions often encounter difficulties caused by sparse data & incomplete system failure knowledge. Pure machine learning or physics-based methods can sometimes be infeasible in such situations. As a result, there has been a growing interest in developing physics-informed machine learning (PIML) models which allow incorporating different forms of physics knowledge at different positions of the machine learning pipeline. This combination provides significant assistance for detection, diagnostic, and prognostics. However, to the best of our knowledge, the bibliometrics analyses and the comprehensive review of the existing research concerning PIML in PHM remain vacant. Our review is therefore dedicated to filling these gaps. We synthesize the concept of PIML in PHM, and propose a taxonomy of PIML approaches from the perspective of “Expression forms of informed knowledge” and “Knowledge informed methods”. The findings and discussions presented in this paper enable us to clarify the current state of the art and the emerging opportunities of PIML approaches, especially for building PHM systems that can work under the “small data and scarce physics knowledge” paradigm.
ISSN:	0307-904X 1872-8480
DOI:	10.1016/j.apm.2023.07.011