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• W3048347447 abstract "• How to break the technical bottlenecks of predictive maintenance by applying the digital twin is discussed • A digital twin-driven cooperative awareness and interconnection framework is proposed to support the awareness of total factors of prediction maintenance decision-making. • The integrated mathematical programming model with considering the parameter uncertainty is established. Predictive maintenance is one of the important technical means to guarantee and improve the normal industrial production. The existing bottlenecks for popularization and application are analyzed. In order to solve these problems, a cooperative awareness and interconnection framework across multiple organizations for total factors that affect prediction maintenance decision-making is discussed. Initially, the structure and operation mechanism of this framework are proposed. It is designed to support the sharing of data, knowledge and resources. As a key supporting technology, the digital twin is also integrated into it to improve the accuracy of fault diagnosis and prediction and support making a maintenance plan with higher accuracy and reliability. Then, under this framework, an integrated mathematical programming model is established with considering the parameter uncertainty and an NSGA-II hybrid algorithm is utilized to solve it. Moreover, an adjustment strategy for a maintenance plan is discussed in response to the dynamic characteristics of the actual maintenance environment. Finally, a case, prediction maintenance decision-making for bearings in grinding rolls of the large vertical mill, is studied. Analysis results verify the advantage of the integrated solving mechanism based on the proposed framework. The framework and integrated decision-making approach can guide the implementation of predictive maintenance with higher accuracy and reliability for industrial enterprises." @default.
• W3048347447 created "2020-08-13" @default.
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• W3048347447 date "2021-01-01" @default.
• W3048347447 modified "2023-10-15" @default.
• W3048347447 title "Prediction maintenance integrated decision-making approach supported by digital twin-driven cooperative awareness and interconnection framework" @default.
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• W3048347447 doi "https://doi.org/10.1016/j.jmsy.2020.08.001" @default.
• W3048347447 hasPublicationYear "2021" @default.
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