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Appl. Sci. 2017, 7(2), 158; doi:10.3390/app7020158

Fault Diagnosis of Roller Bearings Based on a Wavelet Neural Network and Manifold Learning

1,2,3
,
1,2,3
,
4
and
1,2,3,*
1
College of Information Engineering, Capital Normal University, Beijing 100048, China
2
Beijing Engineering Research Center of Highly Reliable Embedded Systems, Capital Normal University, Beijing 100048, China
3
Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, China
4
Information Management Department, Beijing Institute of Petrochemical Technology, Beijing 102617, Beijing, China
*
Author to whom correspondence should be addressed.
Received: 12 December 2016 / Accepted: 7 February 2017 / Published: 8 February 2017
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Abstract

In order to improve the accuracy of the fault diagnosis of roller bearings, this paper proposes a kind of fault diagnosis algorithm based on manifold learning combined with a wavelet neural network. First, a high-dimensional feature signal set is obtained using a conventional feature extraction algorithm; second, an improved Laplacian characteristic mapping algorithm is proposed to reduce the dimensions of the characteristics and obtain an effective characteristic signal. Finally, the processed characteristic signal is inputted into the constructed wavelet neural network whose output is the types of fault. In the actual experiment of recognizing data sets on roller bearing failures, the validity and accuracy of the method for diagnosing faults was verified. View Full-Text
Keywords: roller bearing; manifold learning; wavelet neural network; fault diagnosis roller bearing; manifold learning; wavelet neural network; fault diagnosis
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Wu, L.; Yao, B.; Peng, Z.; Guan, Y. Fault Diagnosis of Roller Bearings Based on a Wavelet Neural Network and Manifold Learning. Appl. Sci. 2017, 7, 158.

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