NDLI: Fault mode identification and analysis of rotating machine in aircraft using neural betwork

Content Provider	IEEE Xplore Digital Library
Author	Liu Hua Zhao Baoqun Zhang Hong
Copyright Year	2008
Description	Author affiliation: Hebei Univ. of Eng., Handan (Liu Hua; Zhao Baoqun; Zhang Hong)
Abstract	To improve the limitation of applying traditional fault diagnosis method to the diagnosis of multi-concurrent vibrant faults for aeroengine in aircraft, a novel approach combining the wavelet transform with self-organizing learning array system is proposed. The effective eigenvectors are acquired by binary discrete orthonormal wavelet transform based on multi-resolution analysis. These feature vectors then are applied to the system for training and testing. The proposed system has three advantageous over a typical neural network: data driven learning, local interconnections and entropy based self-organization. The synthesized method of recursive orthogonal least squares algorithm and improved Givens rotation is used to fulfill the combined network structure and parameter initialization. By means of choosing enough practical samples to verify the proposed network performance and the information representing the faults is inputted into the trained network, and according to the output result the type of fault can be determined. Simulation results and actual applications show that the method can effectively diagnose and analyze the multi-concurrent vibrant fault patterns of aeroengine and the diagnosis result is correct.
Starting Page	482
Ending Page	485
File Size	134645
Page Count	4
File Format	PDF
ISBN	9787900719706
DOI	10.1109/CHICC.2008.4605119
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute Of Intelligence M&C Technical Of KMUST
Subject Keyword	Wavelet transforms Vibrations Fault diagnosis Wavelet transform Rotating machine Neurons Rotors Transforms Neural network Pattern recognition Multiresolution analysis
Content Type	Text
Resource Type	Article

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