Abstract
As an important link to guarantee normal industrial production, equipment maintenance plays an increasingly key role in enhancing the competitiveness of enterprises and supporting green smart manufacturing. This paper aims to promote the implementation of predictive maintenance for complex equipment and improve the green performance of the maintenance service process. A structural framework of information sharing and service network is introduced to build a ubiquitous state data awareness environment for predictive maintenance service. Subsequently, an integrated mathematical problem model that consists of carbon emission objective and extended maintenance cost objective is constructed. Then an improved NSGA-II algorithm is utilized to solve this complicated two-objective optimization problem. In response to deal with the uncertainties of maintenance service environment and inaccuracy of prediction, a data-driven dynamic adjustment strategy is applied. A grinding roll fault case of a large vertical is used to demonstrate the effectiveness of this proposed approach.
Highlights
The complex equipment with characteristics, such as complex product structure, a large number of components, multidisciplinary technologies and long product life cycle, is usually the core of industrial manufacturing process
As the competition among enterprises is becoming cumulatively fierce caused by external market environment changes including globalization and individualized customer needs, timely and effective complex equipment maintenance security is increasingly valued by enterprises
Prediction accuracy and adaptability of maintenance plans limit the implementation of predictive maintenance
Summary
The complex equipment with characteristics, such as complex product structure, a large number of components, multidisciplinary technologies and long product life cycle, is usually the core of industrial manufacturing process. A structural framework of information sharing and service network for target equipment and distributed maintenance resources is introduced to create a ubiquitous maintenance decision and scheduling environment, and types of data need to be collected which impact maintenance service performance and corresponding carbon emissions is described This framework supports a more accurate prediction of performance for predictive maintenance plans based on the real-time interactive capability between the cyberspace layer and the physical space layer. MATHEMATICAL PROBLEM MODELING FOR PREDICTIVE MAINTENANCE DECISION Based on the framework proposed above, associated knowledge data come from real-time fault state of target equipment, the working environment of target equipment and scheduling environment of service resources can be obtained timely to support predictive maintenance decisions Both the economic performance and the environmental performance are concerned, an integrated mathematical model for predictive maintenance decision is established to minimize maintenance cost and the carbon emissions of maintenance scheduling process simultaneously.
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