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Shock and Vibration
Volume 2016 (2016), Article ID 1948029, 14 pages
Research Article

Fault Diagnosis for Rolling Bearing under Variable Conditions Based on Image Recognition

Bo Zhou1,2 and Yujie Cheng1,2

1School of Reliability and Systems Engineering, Beihang University, No. 37, Xueyuan Road, Haidian District, Beijing 100191, China
2Science & Technology on Reliability and Environmental Engineering Laboratory, Beijing 100191, China

Received 25 May 2016; Accepted 14 July 2016

Academic Editor: Minvydas Ragulskis

Copyright © 2016 Bo Zhou and Yujie Cheng. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Rolling bearing faults often lead to electromechanical system failure due to its high speed and complex working conditions. Recently, a large amount of fault diagnosis studies for rolling bearing based on vibration data has been reported. However, few studies have focused on fault diagnosis for rolling bearings under variable conditions. This paper proposes a fault diagnosis method based on image recognition for rolling bearings to realize fault classification under variable working conditions. The proposed method includes the following steps. First, the vibration signal data are transformed into a two-dimensional image based on recurrence plot (RP) technique. Next, a popular feature extraction method which has been widely used in the image field, scale invariant feature transform (SIFT), is employed to extract fault features from the two-dimensional RP and subsequently generate a 128-dimensional feature vector. Third, due to the redundancy of the high-dimensional feature, kernel principal component analysis is utilized to reduce the feature dimensionality. Finally, a neural network classifier trained by probabilistic neural network is used to perform fault diagnosis. Verification experiment results demonstrate the effectiveness of the proposed fault diagnosis method for rolling bearings under variable conditions, thereby providing a promising approach to fault diagnosis for rolling bearings.