Belov, S.; Nikolaev, S.; Uzhinsky, I. Hybrid Data-Driven and Physics-Based Modeling for Gas Turbine Prescriptive Analytics. Int. J. Turbomach. Propuls. Power2020, 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.
Belov, S.; Nikolaev, S.; Uzhinsky, I. Hybrid Data-Driven and Physics-Based Modeling for Gas Turbine Prescriptive Analytics. Int. J. Turbomach. Propuls. Power2020, 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.
