Fault diagnosis of electric rudder system using PSOFOA-BP neural network

Abstract Electric rudder system exerts an immense influence on the movement of aircraft, missiles and ships. To solve the problem of low automation in traditional rudder system, we developed a novel intelligent electric rudder system test equipment, and proposed a new machine learning algorithm for fault diagnosis. This study mainly focuses on the multidimensional data set test and fault diagnosis of the rudder system. Additionally, so as to further the accuracy of the classification model, we optimized back propagation neural network (BPNN) with particle swarm optimization hybrid fruit fly algorithm (PSOFOA), which we called particle swarm optimization hybrid fruit fly algorithm-based back propagation neural network (PSOFOA-BP), and PSOFOA effectively resolved the weights and biases of BPNN. The experimental results indicate that the novel model proposed in this study has higher performance in fault diagnosis of rudder system, which can provides a new concept for rudder testing and production in the future.