A Predictive Maintenance Model for Flexible Manufacturing in the Context of Industry 4.0

Authors: Sang, G.M., Xu, L. and de Vrieze, P.

Journal: Frontiers in Big Data

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. and de Vrieze, P.

Journal: Front Big Data

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. and de Vrieze, P.

Journal: FRONTIERS IN BIG DATA

Volume: 4

eISSN: 2624-909X

DOI: 10.3389/fdata.2021.663466

https://eprints.bournemouth.ac.uk/35933/

Source: Web of Science (Lite)

A Predictive Maintenance Model for Flexible Manufacturing in the Context of Industry 4.0

Authors: Xu, L., De Vrieze, P. and Sang, G.

Journal: Frontiers in Big Data

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. and de Vrieze, P.

Journal: Frontiers in big data

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

A Predictive Maintenance Model for Flexible Manufacturing in the Context of Industry 4.0

Authors: Xu, L. and de Vrieze, P.T.

Journal: Frontiers in Big Data

Volume: 4

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: BURO EPrints