Cargando…
A Predictive Maintenance Model for Flexible Manufacturing in the Context of Industry 4.0
The Industry 4.0 paradigm is the focus of modern manufacturing system design. The integration of cutting-edge technologies such as the Internet of things, cyber–physical systems, big data analytics, and cloud computing requires a flexible platform supporting the effective optimization of manufacturi...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8427870/ https://www.ncbi.nlm.nih.gov/pubmed/34514378 http://dx.doi.org/10.3389/fdata.2021.663466 |
_version_ | 1783750262678093824 |
---|---|
author | Sang, Go Muan Xu, Lai de Vrieze, Paul |
author_facet | Sang, Go Muan Xu, Lai de Vrieze, Paul |
author_sort | Sang, Go Muan |
collection | PubMed |
description | The Industry 4.0 paradigm is the focus of modern manufacturing system design. The integration of cutting-edge technologies such as the Internet of things, cyber–physical systems, big data analytics, and cloud computing requires a flexible platform supporting the effective optimization of manufacturing-related processes, e.g., predictive maintenance. Existing predictive maintenance studies generally focus on either a predictive model without considering the maintenance decisions or maintenance optimizations based on the degradation models of the known system. To address this, we propose PMMI 4.0, a Predictive Maintenance Model for Industry 4.0, which utilizes a newly proposed solution PMS4MMC for supporting an optimized maintenance schedule plan for multiple machine components driven by a data-driven LSTM model for RUL (remaining useful life) estimation. The effectiveness of the proposed solution is demonstrated using a real-world industrial case with related data. The results showed the validity and applicability of this work. |
format | Online Article Text |
id | pubmed-8427870 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84278702021-09-10 A Predictive Maintenance Model for Flexible Manufacturing in the Context of Industry 4.0 Sang, Go Muan Xu, Lai de Vrieze, Paul Front Big Data Big Data The Industry 4.0 paradigm is the focus of modern manufacturing system design. The integration of cutting-edge technologies such as the Internet of things, cyber–physical systems, big data analytics, and cloud computing requires a flexible platform supporting the effective optimization of manufacturing-related processes, e.g., predictive maintenance. Existing predictive maintenance studies generally focus on either a predictive model without considering the maintenance decisions or maintenance optimizations based on the degradation models of the known system. To address this, we propose PMMI 4.0, a Predictive Maintenance Model for Industry 4.0, which utilizes a newly proposed solution PMS4MMC for supporting an optimized maintenance schedule plan for multiple machine components driven by a data-driven LSTM model for RUL (remaining useful life) estimation. The effectiveness of the proposed solution is demonstrated using a real-world industrial case with related data. The results showed the validity and applicability of this work. Frontiers Media S.A. 2021-08-25 /pmc/articles/PMC8427870/ /pubmed/34514378 http://dx.doi.org/10.3389/fdata.2021.663466 Text en Copyright © 2021 Sang, Xu and de Vrieze. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Big Data Sang, Go Muan Xu, Lai de Vrieze, Paul A Predictive Maintenance Model for Flexible Manufacturing in the Context of Industry 4.0 |
title | A Predictive Maintenance Model for Flexible Manufacturing in the Context of Industry 4.0 |
title_full | A Predictive Maintenance Model for Flexible Manufacturing in the Context of Industry 4.0 |
title_fullStr | A Predictive Maintenance Model for Flexible Manufacturing in the Context of Industry 4.0 |
title_full_unstemmed | A Predictive Maintenance Model for Flexible Manufacturing in the Context of Industry 4.0 |
title_short | A Predictive Maintenance Model for Flexible Manufacturing in the Context of Industry 4.0 |
title_sort | predictive maintenance model for flexible manufacturing in the context of industry 4.0 |
topic | Big Data |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8427870/ https://www.ncbi.nlm.nih.gov/pubmed/34514378 http://dx.doi.org/10.3389/fdata.2021.663466 |
work_keys_str_mv | AT sanggomuan apredictivemaintenancemodelforflexiblemanufacturinginthecontextofindustry40 AT xulai apredictivemaintenancemodelforflexiblemanufacturinginthecontextofindustry40 AT devriezepaul apredictivemaintenancemodelforflexiblemanufacturinginthecontextofindustry40 AT sanggomuan predictivemaintenancemodelforflexiblemanufacturinginthecontextofindustry40 AT xulai predictivemaintenancemodelforflexiblemanufacturinginthecontextofindustry40 AT devriezepaul predictivemaintenancemodelforflexiblemanufacturinginthecontextofindustry40 |