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2013 (Vol. 5, Issue: 16)
Article Information:

Fault Diagnosis of Autonomous Underwater Vehicles

Xiao Liang, Jundong Zhang and `Wei Li
Corresponding Author:  Xiao Liang 

Key words:  Autonomous underwater vehicle, least disturbance, thruster fault diagnosis, wavelet neural network, , ,
Vol. 5 , (16): 4071-4076
Submitted Accepted Published
March 23, 2012 January 11, 2013 April 30, 2013

In this study, we propose the least disturbance algorithm adding scale factor and shift factor. 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.
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  Cite this Reference:
Xiao Liang, Jundong Zhang and `Wei Li, 2013. Fault Diagnosis of Autonomous Underwater Vehicles.  Research Journal of Applied Sciences, Engineering and Technology, 5(16): 4071-4076.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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