AUTHOR=Sang Go Muan , Xu Lai , de Vrieze Paul 

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

JOURNAL=Frontiers in Big Data

VOLUME=Volume 4 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2021.663466

DOI=10.3389/fdata.2021.663466

ISSN=2624-909X

ABSTRACT=The Industry 4.0 paradigm is the focus of modern manufacturing system design. The integration of cutting-edge technologies such as 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.