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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| Published in: | Applied mathematical modelling Vol. 124; pp. 325 - 352 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Inc
01.12.2023
Elsevier |
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| ISSN: | 0307-904X, 1872-8480 |
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| Abstract | •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. |
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| AbstractList | 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. •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. |
| Author | MEDJAHER, Kamal GOGU, Christian MORIO, Jérôme NGUYEN, Khanh T.P. DENG, Weikun |
| Author_xml | – sequence: 1 givenname: Weikun orcidid: 0000-0002-5195-4184 surname: DENG fullname: DENG, Weikun email: weikun.deng@enit.fr organization: Laboratoire Génie de Production, LGP, Université de Toulouse, INP-ENIT, 47 Av. d’Azereix, Tarbes, 65016, France – sequence: 2 givenname: Khanh T.P. surname: NGUYEN fullname: NGUYEN, Khanh T.P. organization: Laboratoire Génie de Production, LGP, Université de Toulouse, INP-ENIT, 47 Av. d’Azereix, Tarbes, 65016, France – sequence: 3 givenname: Kamal surname: MEDJAHER fullname: MEDJAHER, Kamal organization: Laboratoire Génie de Production, LGP, Université de Toulouse, INP-ENIT, 47 Av. d’Azereix, Tarbes, 65016, France – sequence: 4 givenname: Christian surname: GOGU fullname: GOGU, Christian organization: Institut Clément Ader (ICA), Université de Toulouse, ISAE-SUPAERO, UPS, CNRS, INSA, Mines Albi, 3 rue Caroline Aigle, Toulouse 31400, France – sequence: 5 givenname: Jérôme surname: MORIO fullname: MORIO, Jérôme organization: ONERA/DTIS, Université de Toulouse, Toulouse F-31055, France |
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| Keywords | 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 |
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| Snippet | •Systematic bibliometric analysis of PIML in PHM.•Novel perspectives for PIML from the “Informed knowledge forms” and “Informed methods”.•Taxonomy of PIML... Prognostics and health management (PHM) plays a constructive role in the equipment’s entire life health service. It has long benefited from intensive research... |
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| Title | Physics-informed machine learning in prognostics and health management: State of the art and challenges |
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