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Thruster Fault Diagnosis of Autonomous Underwater Vehicles Based on Least Disturbance Wavelet Neural Network

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5 Author(s)
Xiao Liang ; Coll. of Marine Eng., Dalian Maritime Univ., Dalian, China ; Wei Li ; Linfang Su ; Han Yin
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Aiming at the character that the hidden layer wavelet function of wavelet neural network can adjust scale factor and shift factor to affect the outputs of neural network, the least disturbance algorithm adding scale factor and shift factor was proposed. The dynamic learning ratio can be calculated to minimize the scale factor and shift factor of wavelet function and the variation of net weights, and the algorithm improve the stability and the convergence of wavelet neural network. It was applied to build the dynamical model of autonomous underwater vehicles, and the residuals are generated by comparing the outputs of the dynamical model with the real state values in the condition of thruster fault. Fault detection rules are distilled by residual analysis to execute thruster fault diagnosis. The results of simulation prove the effectiveness.

Published in:

Computer Modeling and Simulation, 2010. ICCMS '10. Second International Conference on  (Volume:1 )

Date of Conference:

22-24 Jan. 2010