Railway Rolling Bearing Faults Diagnosis Based on Wavelet Packet and EKF Training RBF Neural Network

Based on wavelet packet and extended Kalman filter (EKF) training RBF neural network method, a method for the fault diagnosis of railway rolling bearing is proposed in this paper. The wavelet packet and RBFNN are introduced. The wavelet packet is used to translate raw vibration signals of a railway...

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Bibliographic Details
Published in:Sensors & transducers 2013-11, Vol.158 (11), p.421-421
Main Authors: Wang, Xing, Zhao, Yuan-Jing
Format: Article
Language:English
Subjects:
Neural networks
Packets (communication)
Railroads
Railway engineering
Railways
Roller bearings
Rolling bearings
Wavelet
Online Access:Get full text
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Summary:Based on wavelet packet and extended Kalman filter (EKF) training RBF neural network method, a method for the fault diagnosis of railway rolling bearing is proposed in this paper. The wavelet packet and RBFNN are introduced. The wavelet packet is used to translate raw vibration signals of a railway rolling bearing into time-scale representation. Then, the wavelet packet energy eigenvector is constructed, next, those wavelet packet energy eigenvectors as fault samples for training RBF neural network. To ameliorate the algorithm, EKF is exploited to optimize the algorithm so as to determine the best values for "network connection weight", finally the fault patterns of the railway rolling bearings are identified. The results show that the proposed method is superior to the RBF neural network in extracting the fault characteristics of roller bearings. This method is effective and can be used for automotive recognition to rotary machine faults.
ISSN:2306-8515
1726-5479
1726-5479