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

Content Provider	IEEE Xplore Digital Library
Author	Xiao Liang Wei Li Linfang Su Han Yin Jun Zhao
Copyright Year	2010
Abstract	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.
Starting Page	78
Ending Page	82
File Size	434514
Page Count	5
File Format	PDF
ISBN	9781424456420
e-ISBN	9781424456437
DOI	10.1109/ICCMS.2010.8
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-01-22
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stability Heuristic algorithms Educational institutions least disturbance Vehicle dynamics Underwater vehicles wavelet neural network Automotive engineering Convergence thruster fault diagnosis Fault diagnosis Fault detection Neural networks autonomous underwater vehicle
Content Type	Text
Resource Type	Article

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