Aircraft Engine Gas Path Fault Diagnosis Based on Hybrid PSO-TWSVM

Abstract

Twin support vector machine (TWSVM) is a new development of support vector machine (SVM) algorithm. It has the smaller computation scale and the stronger ability to cope with unbalanced problems. In this paper, TWSVM is introduced into aircraft engine gas path fault diagnosis. The generalization capacity of Gauss kernel function usually used in TWSVM is relatively weak. So a mixed kernel function is used to improve performance to ensure that the TWSVM algorithm can better balance a strong generalization ability and a good learning ability. Experimental results prove that the cross validation training accuracy of TWSVM using the mixed kernel function averagely increases 2%. Grid search is usually applied in parameter optimization of TWSVM, but it heavily depends on experience. Therefore, the hybrid particle swarm algorithm is introduced. It can intelligently and rapidly find the global optimum. Experiments prove that its training accuracy is better than that of the classical particle swarm algorithm by 5%.