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Challenges and Opportunities of System-Level Prognostics

Prognostics and health management (PHM) has become an essential function for safe system operation and scheduling economic maintenance. To date, there has been much research and publications on component-level prognostics. In practice, however, most industrial systems consist of multiple components...

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Detalles Bibliográficos
Autores principales: Kim, Seokgoo, Choi, Joo-Ho, Kim, Nam H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8625866/
https://www.ncbi.nlm.nih.gov/pubmed/34833731
http://dx.doi.org/10.3390/s21227655
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author Kim, Seokgoo
Choi, Joo-Ho
Kim, Nam H.
author_facet Kim, Seokgoo
Choi, Joo-Ho
Kim, Nam H.
author_sort Kim, Seokgoo
collection PubMed
description Prognostics and health management (PHM) has become an essential function for safe system operation and scheduling economic maintenance. To date, there has been much research and publications on component-level prognostics. In practice, however, most industrial systems consist of multiple components that are interlinked. This paper aims to provide a review of approaches for system-level prognostics. To achieve this goal, the approaches are grouped into four categories: health index-based, component RUL-based, influenced component-based, and multiple failure mode-based prognostics. Issues of each approach are presented in terms of the target systems and employed algorithms. Two examples of PHM datasets are used to demonstrate how the system-level prognostics should be conducted. Challenges for practical system-level prognostics are also addressed.
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spelling pubmed-86258662021-11-27 Challenges and Opportunities of System-Level Prognostics Kim, Seokgoo Choi, Joo-Ho Kim, Nam H. Sensors (Basel) Review Prognostics and health management (PHM) has become an essential function for safe system operation and scheduling economic maintenance. To date, there has been much research and publications on component-level prognostics. In practice, however, most industrial systems consist of multiple components that are interlinked. This paper aims to provide a review of approaches for system-level prognostics. To achieve this goal, the approaches are grouped into four categories: health index-based, component RUL-based, influenced component-based, and multiple failure mode-based prognostics. Issues of each approach are presented in terms of the target systems and employed algorithms. Two examples of PHM datasets are used to demonstrate how the system-level prognostics should be conducted. Challenges for practical system-level prognostics are also addressed. MDPI 2021-11-18 /pmc/articles/PMC8625866/ /pubmed/34833731 http://dx.doi.org/10.3390/s21227655 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Kim, Seokgoo
Choi, Joo-Ho
Kim, Nam H.
Challenges and Opportunities of System-Level Prognostics
title Challenges and Opportunities of System-Level Prognostics
title_full Challenges and Opportunities of System-Level Prognostics
title_fullStr Challenges and Opportunities of System-Level Prognostics
title_full_unstemmed Challenges and Opportunities of System-Level Prognostics
title_short Challenges and Opportunities of System-Level Prognostics
title_sort challenges and opportunities of system-level prognostics
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8625866/
https://www.ncbi.nlm.nih.gov/pubmed/34833731
http://dx.doi.org/10.3390/s21227655
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