Wuttke, A.; Hunker,J,; Rabe, M.; Diepenbrock, J.-P.: Estimating Parameters with Data Farming for Condition-based Maintenance in a Digital Twin. In: Corlu, C.G.; Hunter, S.R.; Lam, H.; Onggo, S.; Shortle, J.; Biller, B. (eds.): Proceedings of the Winter Simulation Conference 2023. Piscataway, NJ: IEEE 2023, pp. 1641-1652.

Nowadays, vast amounts of data can be collected by sensors and used for data-driven approaches. Digital twins provide a framework to exploit these data for solving various issues. For many companies in the industrial sector, machine maintenance is a significant issue. Maintenance is essential for high overall equipment efficiency, but it can also be costly. Therefore, it should only be performed when necessary, based on the machine’s condition. Condition monitoring is used to assess a machine’s condition periodically, allowing for condition-based maintenance. In this paper, a simulation-based approach for parameter estimation is presented that contributes to condition-based maintenance. It introduces condition indicators for certain features of machines and demonstrates how to evaluate them using data farming, which employs simulation models as data generators. Additionally, the implementation of this approach in digital twins is discussed.