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.
Highlights
Modern collaborative industry is moving toward applying the Industry 4.0 concept for achieving effective smart solutions (Zezulka et al, 2016; Thoben et al, 2017; Sang et al, 2020)
Our work focused on the design and development of a predictive maintenance model for Industry 4.0 (i.e., Predictive Maintenance for Industry 4.0) which utilizes the proposed predictive maintenance scheduling for multiple machine components (i.e., Predictive Maintenance Schedule for Multiple Machines and Components) by taking into account machine data such as operation, condition, and maintenance data
FIRST’s industrial manufacturing case is used to demonstrate the effectiveness of PMMI 4.0 and PMS4MMC implementing FIWARE Cosmos Big Data Analytics to support a flexible Industry 4.0 platform in FIRST Flexible Manufacturing Case
Summary
Modern collaborative industry is moving toward applying the Industry 4.0 concept for achieving effective smart solutions (Zezulka et al, 2016; Thoben et al, 2017; Sang et al, 2020). In Industry 4.0 focusing on the manufacturing context, business processes are executed across different factories and enterprises This enables the collaborative chain to manage the production life cycle and demands effectively (Xu et al, 2020) as well as providing opportunities for supporting data-driven predictive maintenance within one organization as well as cross organizations (Sang et al, 2020a). Industry 4.0 Predictive Maintenance unpredicted (Mobley 2002) The latter focuses on a data-driven approach utilizing various data produced by machine equipment tools, factory operation, and other information processing systems (Mobley 2002; Sang et al, 2020a). The results of the case are discussed in Discussion to conclude in Conclusion and Future Work
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