The methodology for prescriptive maintenance of complex technical systems is presented. The proposed methodology is based on a hybrid physics-based and data-driven modelling of complex systems. This approach integrates traditional physics-based simulation techniques such as finite-element modelling, finite-volume modelling, bond-graph modelling and data-driven models, with machine learning algorithms. Combined implementation of the both approaches results in the development of a set of reliable, fast and continuously updating models of technical systems applicable for predictive and prescriptive analytics. The methodology is demonstrated on the jet-engine power plant preventive maintenance case-study.
CITATION STYLE
Nikolaev, S., Belov, S., Gusev, M., & Uzhinsky, I. (2019). Hybrid Data-Driven and Physics-Based Modelling for Prescriptive Maintenance of Gas-Turbine Power Plant. In IFIP Advances in Information and Communication Technology (Vol. 565 IFIP, pp. 379–388). Springer. https://doi.org/10.1007/978-3-030-42250-9_36
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