Journal
FRONTIERS IN BIG DATA
Volume 4, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fdata.2021.663466
Keywords
Industry 4; 0; predictive maintenance; big data analytics; maintenance schedule plan; flexible manufacturing
Categories
Funding
- European Union [734599]
- Marie Curie Actions (MSCA) [734599] Funding Source: Marie Curie Actions (MSCA)
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The Industry 4.0 paradigm focuses on the integration of cutting-edge technologies for effective optimization of manufacturing processes, such as predictive maintenance. To address the limitations of existing predictive maintenance studies, PMMI 4.0 is proposed with a new solution PMS4MMC for supporting optimized maintenance plans for multiple machine components.
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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