Abstract
Maintenance activities of machines in the manufacturing industry are essential to keep machine availability as high as possible. A breakdown of a single machine can lead to a complete production stop. Maintenance is traditionally performed by predefined maintenance specifications of the machine manufacturers. With the help of condition- based maintenance, maintenance intervals can be optimized due to detailed knowledge through sensor data. This results in an adapted maintenance schedule where machines are only maintained when necessary. Apart from time savings, this also reduces costs. An decision support system with optimization model for maintenance planning is developed considering the right balance between the probabilities of failure of the machines and the potential breakdown costs. The current conditions of the machines are used to forecast the necessary maintenance activities for several periods. The decision support system helps maintenance planners to choose their decision-making horizon flexibly.
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References
Bousdekis, A., Magoutas, B., Apostolou, D., & Mentzas, G. (2015). Review, analysis and synthesis of prognostic-based decision support methods for condition based maintenance. Journal of Intelligent Manufacturing, 26, 1–14.
Bouvard, K., Artus, S., Berenguer, C., & Cocquempot, V. (2011). Condition-based dynamic maintenance operations planning and grouping. Application to commercial heavy vehicles. Reliability Engineering and System Safety, 96, 601–610.
Kaiser, K., & Gabraeel, N. (2009). Predictive maintenance management using sensor-based degradation models. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 39(4), 840–849.
Kothamasu, R., Huang, S. H., & VerDuin, W. H. (2006). System health monitoring and prognostics - a review of current paradigms and practices. The International Journal of Advanced Manufacturing Technology, 28, 1012–1024.
Peng, Y., Dong, M., & Zuo, M. (2010). Current status of machine prognostics in condition-based maintenance: A review. The International Journal of Advanced Manufacturing Technology, 50, 297–313.
Wildeman, R., Dekker, R., & Smit, A. (1997). A dynamic policy for grouping maintenance activities. European Journal of Operational Research, 99(3), 530–551.
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Olivotti, D., Passlick, J., Dreyer, S., Lebek, B., Breitner, M.H. (2018). Maintenance Planning Using Condition Monitoring Data. In: Kliewer, N., Ehmke, J., Borndörfer, R. (eds) Operations Research Proceedings 2017. Operations Research Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-319-89920-6_72
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DOI: https://doi.org/10.1007/978-3-319-89920-6_72
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Online ISBN: 978-3-319-89920-6
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