A Component Selection Method for Prioritized Predictive Maintenance

Abstract : Predictive maintenance is a maintenance strategy of diagnosing and prognosing a machine based on its condition. Compared with other maintenance strategies, the predictive maintenance strategy has the advantage of lowering the maintenance cost and time. Thus, many studies have been conducted to develop a predictive maintenance model based on a growth of prediction methodology. However, these studies tend to focus on building the predictive model and measuring its performance, rather than selecting the appropriate components for predictive maintenance. Nevertheless, selecting the predictive maintenance policy and target component are as important as model selection and performance measurement. In this paper, a selection method is proposed to improve component selection by referencing current literature and industry expert knowledge. The results of this research can serve as a foundation for further studies in this area.
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Communication dans un congrès
Hermann Lödding; Ralph Riedel; Klaus-Dieter Thoben; Gregor von Cieminski; Dimitris Kiritsis. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2017, Hamburg, Germany. Springer International Publishing, IFIP Advances in Information and Communication Technology, AICT-513 (Part I), pp.433-440, 2017, Advances in Production Management Systems. The Path to Intelligent, Collaborative and Sustainable Manufacturing 〈10.1007/978-3-319-66923-6_51〉
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Bongjun Ji, Hyunseop Park, Kiwook Jung, Seung Bang, Minchul Lee, et al.. A Component Selection Method for Prioritized Predictive Maintenance. Hermann Lödding; Ralph Riedel; Klaus-Dieter Thoben; Gregor von Cieminski; Dimitris Kiritsis. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2017, Hamburg, Germany. Springer International Publishing, IFIP Advances in Information and Communication Technology, AICT-513 (Part I), pp.433-440, 2017, Advances in Production Management Systems. The Path to Intelligent, Collaborative and Sustainable Manufacturing 〈10.1007/978-3-319-66923-6_51〉. 〈hal-01666215〉

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