A Comparative Study on Methods for Fusing Data-Driven and Physics-Based Models for Hybrid Remaining Useful Life Prediction of Air Filters-Reference-Cited by-同舟云学术

A Comparative Study on Methods for Fusing Data-Driven and Physics-Based Models for Hybrid Remaining Useful Life Prediction of Air Filters

Published:2023 Issue: Volume:11 Page:35737-35753
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Hagmeyer Simon¹^ORCID,Zeiler Peter¹

Affiliation:

1. Research Group for Reliability Engineering and Prognostics and Health Management, Faculty of Mechanical and Systems Engineering, Esslingen University of Applied Sciences, Esslingen, Germany

Funder

German Federal Ministry of Education and Research through the Program “Professorales Personal an Fachhochschulen”

Baden-Württemberg Ministry of Science, Research and Culture, Germany

Esslingen University of Applied Sciences in the Funding Program Open Access Publishing

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10097731.pdf?arnumber=10097731

Reference73 articles.

1. Potential, challenges and future directions for deep learning in prognostics and health management applications

2. Description of the clogging and cleaning cycles of filter media

3. Toward Causal Representation Learning

4. Theoretical Model of Single Fiber Efficiency and the Effect of Microstructure on Fibrous Filtration Performance: A Review

5. A Survey of Deep Active Learning

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