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A Predictive Maintenance Model for Flexible Manufacturing in the Context of Industry 4.0
Go Muan Sang1, Lai Xu1, Paul de Vrieze1
1Faculty of Science and Technology, Bournemouth University, Poole, United Kingdom.
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.
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