A Predictive Maintenance Model for Flexible Manufacturing in the Context of Industry 4.0
Authors: Sang, G.M., Xu, L., de Vrieze, P.
Journal: Frontiers in Big Data
Publication Date: 25/08/2021
Volume: 4
eISSN: 2624-909X
DOI: 10.3389/fdata.2021.663466
Abstract: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.
https://eprints.bournemouth.ac.uk/35933/
Source: Scopus
A Predictive Maintenance Model for Flexible Manufacturing in the Context of Industry 4.0.
Authors: Sang, G.M., Xu, L., de Vrieze, P.
Journal: Front Big Data
Publication Date: 2021
Volume: 4
Pages: 663466
eISSN: 2624-909X
DOI: 10.3389/fdata.2021.663466
Abstract: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.
https://eprints.bournemouth.ac.uk/35933/
Source: PubMed
A Predictive Maintenance Model for Flexible Manufacturing in the Context of Industry 4.0
Authors: Sang, G.M., Xu, L., de Vrieze, P.
Journal: FRONTIERS IN BIG DATA
Publication Date: 25/08/2021
Volume: 4
eISSN: 2624-909X
DOI: 10.3389/fdata.2021.663466
https://eprints.bournemouth.ac.uk/35933/
Source: Web of Science
A Predictive Maintenance Model for Flexible Manufacturing in the Context of Industry 4.0
Authors: Xu, L., De Vrieze, P.
Journal: Frontiers in Big Data
Publication Date: 25/08/2021
Volume: 4
Publisher: Frontiers
eISSN: 2624-909X
DOI: 10.3389/fdata.2021.663466
Abstract: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.
https://eprints.bournemouth.ac.uk/35933/
https://www.frontiersin.org/journals/big-data
Source: Manual
A Predictive Maintenance Model for Flexible Manufacturing in the Context of Industry 4.0.
Authors: Sang, G.M., Xu, L., de Vrieze, P.
Journal: Frontiers in big data
Publication Date: 01/2021
Volume: 4
Pages: 663466
eISSN: 2624-909X
ISSN: 2624-909X
DOI: 10.3389/fdata.2021.663466
Abstract: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.
https://eprints.bournemouth.ac.uk/35933/
Source: Europe PubMed Central