Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Hybrid Data-Driven and Physics-Based Modelling for Gas-Turbine Prescriptive Analytics

Version 1 : Received: 17 September 2020 / Approved: 20 September 2020 / Online: 20 September 2020 (13:48:48 CEST)

How to cite: Belov, S.; Nikolaev, S.; Uzhinsky, I. Hybrid Data-Driven and Physics-Based Modelling for Gas-Turbine Prescriptive Analytics. Preprints 2020, 2020090460 (doi: 10.20944/preprints202009.0460.v1). Belov, S.; Nikolaev, S.; Uzhinsky, I. Hybrid Data-Driven and Physics-Based Modelling for Gas-Turbine Prescriptive Analytics. Preprints 2020, 2020090460 (doi: 10.20944/preprints202009.0460.v1).

Abstract

This paper presents a methodology for predictive and prescriptive analytics of complex engineering systems. The methodology is based on a combination of physics-based and data-driven modeling using machine learning techniques. Combining these approaches results in a set of reliable, fast, and continuously updating models for prescriptive analytics. The methodology is demonstrated with a case study of a jet-engine power plant preventive maintenance and diagnostics of its flame tube.

Subject Areas

hybrid modelling; prescriptive analytics; gas engine; machine learning

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