Study on fault diagnosis algorithms of EHA based on CNN-SVM

Abstract

Contrapose the highly integrated, complex working conditions and many kinds of faults of aircraft electro hydrostatic actuator(EHA), to diagnose the typical fault of EHA effectively, a fault diagnosis algorithm based on convolutional neural networks (CNN) and support vector machine(SVM) was proposed. Firstly, the fault date sets are entered on CNN for adaptive feature extraction, then the output of the fully connected layer of CNN are classified by using SVM. To improve the performance of SVM, dynamic inertia weight adaptive particle swarm optimization (IWAPSO) was used to optimize the SVM parameters. Finally, the sensitivity of SVM to noise was reduced by introducing ramp loss function. The results show that the accuracy of SVM after parameter optimization is 12.6% higher than that of standard SVM and 17.3% higher than CNN. The SVM based on the ramp loss function showed better robustness when using noisy test sets.