Article
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Preserved in Portico This version is not peer-reviewed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)
A peer-reviewed article of this Preprint also exists.
Belov, S.; Nikolaev, S.; Uzhinsky, I. Hybrid Data-Driven and Physics-Based Modeling for Gas Turbine Prescriptive Analytics. Int. J. Turbomach. Propuls. Power 2020, 5, 29. Belov, S.; Nikolaev, S.; Uzhinsky, I. Hybrid Data-Driven and Physics-Based Modeling for Gas Turbine Prescriptive Analytics. Int. J. Turbomach. Propuls. Power 2020, 5, 29.
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.
Keywords
hybrid modelling; prescriptive analytics; gas engine; machine learning
Subject
Engineering, Mechanical Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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