method for detecting gas path fault of gas turbine based on Bayesian network model

A technology of Bayesian networks and gas turbines, which is applied in computer parts, character and pattern recognition, data processing applications, etc., and can solve the problems of lack of structure learning and parameter learning methods

Inactive Publication Date: 2019-03-22
SHANGHAI JIAO TONG UNIV +1
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  • Abstract
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Problems solved by technology

Most of the existing technologies can only detect failure modes, or lack targeted structure learning and parameter learning methods, and do not start from the actual needs of gas turbine fault detection

Method used

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  • method for detecting gas path fault of gas turbine based on Bayesian network model
  • method for detecting gas path fault of gas turbine based on Bayesian network model
  • method for detecting gas path fault of gas turbine based on Bayesian network model

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Embodiment Construction

[0037] Such as image 3 As shown, it is a gas turbine gas path fault detection system based on the Bayesian network model involved in this embodiment, including: a signal acquisition module, a Bayesian network training module and a Bayesian network testing module, wherein: signal acquisition The module inputs the data of gas turbine gas circuit components collected in real time, the Bayesian network training module is connected to the signal acquisition module and transmits the discretized optimized training set and optimized test set, and the Bayesian network test module and Bayesian network training The modules are connected and transmit the Bayesian network after initialization and parameter optimization, and the Bayesian network test module outputs the Bayesian network model with the best inference accuracy.

[0038] Such as figure 1 and figure 2 As shown, in this embodiment, the data of the gas turbine gas circuit components obtained by real-time signal collection is u...

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Abstract

A method for detecting gas path fault of gas turbine based on Bayesian network model is provided. The method comprises the following steps: the data of gas path components of the gas turbine obtainedby real-time signal acquisition is generated into a data set, normal operating parameters are obtained from the data set, Abnormal parameters to be measured, Set and Test Set are trained, the optimaltraining set and optimal test set are obtained by pretreatment and clustering analysis, Then the optimized Bayesian network model for real-time detection of the current operating conditions of the gasturbine system is obtained by testing the initialized and parameter optimized Bayesian network with the optimized test suite, so as to detect system failures. The invention provides a specific Bayesian network model structure learning and parameter learning method, and further establishes a correlation model between measured parameters and gas turbine normal operating condition parameters, and realizes on-line fault detection of gas turbine gas path system.

Description

technical field [0001] The invention relates to a technology in the field of thermal power generation, in particular to a method for detecting gas path failures of a gas turbine based on a Bayesian network model. Background technique [0002] The structure of the gas turbine is complex, and it operates under the conditions of high speed, high temperature, high pressure and high stress for a long time. The working environment is harsh, and it is prone to mechanical failure and gas circuit failure. The gas turbine is composed of gas circuit components and auxiliary systems. If the gas circuit components of the gas turbine fail, the availability of the gas turbine will be seriously affected. Therefore, it is necessary to perform fault detection on the gas circuit components of the gas turbine to detect equipment abnormalities in advance and effectively prevent failures due to fault expansion. In order to ensure the safe and stable operation of the gas turbine. For gas turbines...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/46G06Q50/06
CPCG06Q50/06G06V10/44G06F18/23213G06F18/24155G06F18/214
Inventor 夏唐斌徐伟司国锦周骏史周郑宇
Owner SHANGHAI JIAO TONG UNIV
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