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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Applied mathematical modelling Jg. 124; S. 325 - 352
Hauptverfasser:	DENG, Weikun, NGUYEN, Khanh T.P., MEDJAHER, Kamal, GOGU, Christian, MORIO, Jérôme
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Inc 01.12.2023 Elsevier
Schlagworte:	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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!