A Predictive Maintenance Model for Flexible Manufacturing in the Context of Industry 4.0 - Sang, Go Muan; Xu, Lai; de Vrieze, Paul (Frontiers in Big Data, 2021)
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.