{"title":"Gas path diagnosis method for gas turbine fusing performance analysis models and extreme learning machine","authors":"Shiyao Li, Zhenlin Li, Meng Zhang, Song Han","doi":"10.2298/tsci220509018l","DOIUrl":null,"url":null,"abstract":"The gas path analysis, which can quantify the performance degradation of gas turbine components, has been extensively applied to the gas path diagnosis. However, the precondition of this method is that the number of measurable parameters for the gas turbine to be diagnosed should not be less than the number of its health factors. In the existing research, this precondition can be guaranteed through common approaches such as screening the degraded components by a model-based prediagnosis process or recognizing the degraded components by using tools such as an artificial neural network or a support vector machine. However, the diagnosis speed, recognition accuracy, and robustness of these approaches need to be improved. Therefore, a diagnosis method fusing the gas path performance analysis model and the extreme learning machine was proposed in this paper and applied to a GE LM2500+SAC gas turbine. The working mechanism of similarity ranking-gas path diagnosis-rationality check was introduced in the fusion method, endowing it with a higher recognition accuracy rate, stronger robustness, and higher diagnostic accuracy.","PeriodicalId":23125,"journal":{"name":"Thermal Science","volume":null,"pages":null},"PeriodicalIF":1.1000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Thermal Science","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.2298/tsci220509018l","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"THERMODYNAMICS","Score":null,"Total":0}
引用次数: 0
Abstract
The gas path analysis, which can quantify the performance degradation of gas turbine components, has been extensively applied to the gas path diagnosis. However, the precondition of this method is that the number of measurable parameters for the gas turbine to be diagnosed should not be less than the number of its health factors. In the existing research, this precondition can be guaranteed through common approaches such as screening the degraded components by a model-based prediagnosis process or recognizing the degraded components by using tools such as an artificial neural network or a support vector machine. However, the diagnosis speed, recognition accuracy, and robustness of these approaches need to be improved. Therefore, a diagnosis method fusing the gas path performance analysis model and the extreme learning machine was proposed in this paper and applied to a GE LM2500+SAC gas turbine. The working mechanism of similarity ranking-gas path diagnosis-rationality check was introduced in the fusion method, endowing it with a higher recognition accuracy rate, stronger robustness, and higher diagnostic accuracy.
期刊介绍:
The main aims of Thermal Science
to publish papers giving results of the fundamental and applied research in different, but closely connected fields:
fluid mechanics (mainly turbulent flows), heat transfer, mass transfer, combustion and chemical processes
in single, and specifically in multi-phase and multi-component flows
in high-temperature chemically reacting flows
processes present in thermal engineering, energy generating or consuming equipment, process and chemical engineering equipment and devices, ecological engineering,
The important characteristic of the journal is the orientation to the fundamental results of the investigations of different physical and chemical processes, always jointly present in real conditions, and their mutual influence. To publish papers written by experts from different fields: mechanical engineering, chemical engineering, fluid dynamics, thermodynamics and related fields. To inform international scientific community about the recent, and most prominent fundamental results achieved in the South-East European region, and particularly in Serbia, and - vice versa - to inform the scientific community from South-East European Region about recent fundamental and applied scientific achievements in developed countries, serving as a basis for technology development. To achieve international standards of the published papers, by the engagement of experts from different countries in the International Advisory board.