Fault diagnosis of aero-engine gas path based on GA-LSSVM

Abstract

Abstract: Aiming at the lack of data sample and nonlinearity of aero-engine gas-path fault, the genetic algorithm (GA) optimized least square support vector machine (LSSVM) is applied to aero-engine gas-path fault diagnosis. Firstly, the key parameters of LSSVM algorithm is analyzed, and the regularization parameter(C) and kernel parameter ( g ) of LSSVM are optimited with GA. Secondly, GA-LSSVM is used to diagnose the gas path fault of a biaxial turbofan engine. Finally, SVM, LSSVM, GA-SVM and GA-LSSVM are compared from three aspects of diagnosis accuracy, anti-noise ability and training time. Results show that GA-LSSVM is superior to the other three algorithms in diagnosis accuracy and anti noise intensity, but its training time is longer due to the optimization process of GA-LSSVM.

Key words: aero-engine, gas path fault diagnosis, least square support vector machine(LSSVM), genetic algorithm (GA)

CLC Number:

V233.7

TIAN Jing, HU Hexiang. Fault diagnosis of aero-engine gas path based on GA-LSSVM [J]. Journal of Civil Aviation University of China, 2021, 39(3): 29-33.

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