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Recent deep learning models for diagnosis and health monitoring: a review of researches and future challenges
School of Automation, Wuhan University of Technology, Wuhan, China.
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).
School of Automation, Wuhan University of Technology, Wuhan, China.
2023 (English)In: Transactions of the Institute of Measurement and Control, ISSN 0142-3312, E-ISSN 1477-0369Article in journal (Refereed) Epub ahead of print
Abstract [en]

As an important branch of machine learning, deep learning (DL) models with multiple hidden layer structures have the ability to extract highly representative features from the input. At present, fault detection and diagnosis (FDD) and health monitoring solutions developed based on DL models have received extensive attention in academia and industry along with the rapid improvement of computing power. Therefore, this paper focuses on a comprehensive review of DL model–based FDD and health monitoring schemes in view of common problems of industrial systems. First, brief theoretical backgrounds of basic DL models are introduced. Then, related publications are discussed about the development of DL and graphical models in the industrial context. Afterwards, public data sets are summarized, which are associated with several research papers. More importantly, suggestions on DL model–based diagnosis and health monitoring solutions and future developments are given. Our work will have a positive impact on the selection and design of FDD solutions based on DL and graphical models in the future. © The Author(s) 2023.

Place, publisher, year, edition, pages
London: Sage Publications, 2023.
Keywords [en]
Deep learning, graphical probabilistic models, health monitoring, fault diagnosis, big data
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:hh:diva-49884DOI: 10.1177/01423312231157118ISI: 000950021400001Scopus ID: 2-s2.0-85150937696OAI: oai:DiVA.org:hh-49884DiVA, id: diva2:1731829
Available from: 2023-01-29 Created: 2023-01-29 Last updated: 2023-04-21Bibliographically approved

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Atoui, M. Amine

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