Fault Diagnosis of UV's Sensors Based on Wavelet Neural Network

A fault diagnosis method based on wavelet neural network was proposed to diagnose the fault of sensors used in underwater vehicle(UV). It was observed that the majority of the faulty signals' energy was concentrated in the lower frequency range.But,if this frequency distribution was taken directly as the basis on which to train the system and to distinguish the fault signals form the normal ones or to distinguish one kind of fault from another,it would take much long time of the system and real-time ability of the monitoring system would be ruined.A new method was proposed to carry on the classification with RBF neural network according to the rest part of signals which was normalized after the energy of lower frequency range was abandoned.With the nodes energy differences form wavelet decomposition algorithm,feature extraction characteristics the self-learning ability,after being trained with a large number of samples,the neural network could distinguish five kinds of fault and normal signals with high resolutions.The simulation results proved the simple and easy use of the method,which is suitable for the fault diagnosis of the sensors in UV systems.