Navigation Sensors Fault Diagnosis Based on Wavelet Transformation and Neural network

Abstract

Fault diagnosis plays an important part role in ensure the reliability of integrated navigation, the paper was proposed especially for the integrated navigation which used on ship. How to keep its safety must confront to us. Now most of fault diagnosis are based on traditional methods, Because wavelet transformation can localize signals both in time and frequency, and neural network has the ability of self-organized, self-learning. To make fault diagnosis more effective and can adapt to the development of technology. The paper proposed a more intelligent method which combined wavelet transformation and neural network to enhance the efficiency, the summary procedures were designed as below: we combined the wavelet transformation and neural network in series. After three-layer wavelet decomposition, the coefficients of the third layers are achieved. Then 8 eigenvectors as fault samples to train three-layer RBF neural network, for RBF network is good at classification, after training the network can detect a fault on-line. At the same time, it can classify faults and alarmed the users. Gyro signals are chosen as the simulation inputs, the results prove the method applicability and effectiveness.