Fault Detection of Liquid-Propellant Rocket Engines Based on LSSVM

Abstract

In terms of fault diagnosis of liquid-propellant rocket engine, the fault diagnosis accuracy of the traditional method is low the characteristics of engine fault data are small sample and nonlinear variation. In order to improve the accuracy of sensor fault diagnosis and overcome the scarcity of related samples, the particle swarm optimization (PSO) with strong global searching ability is used to optimize the LS-SVM parameters, and the LSSVM optimal parameter values are obtained by iteration to improve the model fitting accuracy and generalization ability. At the same time, the traditional support vector machine and BP neural network model are used for the detection. The simulation results show that the least squares support vector machine (SVM) detection method based on particle swarm optimization has the advantages of high precision and high speed. It has certain effect and positive significance for improving the safety of liquid-propellant rocket engine test and engine failure loss.