Wavelet neural network applied to fault diagnosis of underwater vehicle

To aim at the character that the uncertainties of the complex system of Autonomous Underwater Vehicle (AUV) bring to model the system difficult, a wavelet neural network (WNN) is proposed to construct the motion model of AUV. The adjustment of the scale factor and shift factor of wavelet and weights of WNN is studied. The WNN has the ability not only to approach the whole figure of a function but also to catch detail changes of the function, which makes the approaching effect preferably. Residuals are achieved by comparing the output of WNN with the sensor output. Fault detection rules are distilled from the residuals to execute thruster fault diagnosis. The feasibility of the method presented is validated by simulation experiment and sea trial results.