Digital twin enabled industry 4.0 predictive maintenance under reliability-centred strategy
This paper introduces the idea of implementing digital twin for the purpose predictive maintenance under open system architecture for condition based maintenance. An appropriate predictive maintenance (PdM) is critical to machines operating under a complex working conditions; in order to prevent maj...
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| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | en en |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2022
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| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/32829/1/7.%20Digital%20twin%20enabled%20industry%204.0%20predictive%20maintenance%20under%20reliability-centred%20strategy.pdf https://umpir.ump.edu.my/id/eprint/32829/2/7.1%20Digital%20twin%20enabled%20industry%204.0%20predictive%20maintenance%20under%20reliability-centred%20strategy.pdf https://umpir.ump.edu.my/id/eprint/32829/ https://doi.org/10.1109/ICEEICT53079.2022.9768590 |
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| Summary: | This paper introduces the idea of implementing digital twin for the purpose predictive maintenance under open system architecture for condition based maintenance. An appropriate predictive maintenance (PdM) is critical to machines operating under a complex working conditions; in order to prevent major safety accidents and casualties. A cost and reliability optimized predictive maintenance framework for industry 4.0 machines key parts based on qualitative and quantitative analysis of monitoring data is proposed. Employing machine learning and advanced analytics for data fusion for PdM promises for an accurate failure diagnostics and prognostics in addition to the optimized maintenance decisions. Furthermore, a cost effective maintenance framework can be implemented under reliability centered maintenance strategy. The decision-making that guides predictive maintenance can be obtained based on the synthesis of qualitative and quantitative analysis. The proposed method is expected to provide cost-effective maintenance and improved intelligence of predictive process and the accuracy of predictive results |
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