NDLI: Fiber Optical Gyro Fault Diagnosis based on Wavelet Transform and Neural Network

Content Provider	IEEE Xplore Digital Library
Author	Huaming Qian Zhenlv Zhang Jichen Ma
Copyright Year	2008
Description	Author affiliation: Coll. of Autom., Harbin Eng. Univ., Harbin (Huaming Qian; Zhenlv Zhang; Jichen Ma)
Abstract	Fault diagnosis plays an important role in detecting the reliability of integrated navigation. This paper proposed an intelligent method which combined wavelet transform with neural network to enhance efficiency. The combined method between wavelet transform and neural network was in series. Based on Daubechies, wavelet symmetry had been constructed. Through Daubechies eight-layer wavelet decomposing, detailed information of eight layers was achieved. Then the 8-dimensional eigenvector was used to train three-layer RBF neural network as fault sample. For RBF network is good at classifying, the network can detect a fault on-line after training. At the same time, it can classify faults and alarm. Gyro signals were chosen as the simulation inputs, the results indicated the method's applicability and effectiveness.
Starting Page	608
Ending Page	611
File Size	1170564
Page Count	4
File Format	PDF
ISBN	9781424423675
DOI	10.1109/MESA.2008.4735743
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault diagnosis Wavelet transforms Intelligent networks Navigation Fault detection Neural networks Optical computing Radial basis function networks Optical fiber networks Telecommunication network reliability
Content Type	Text
Resource Type	Article

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