Fault Diagnosis for Rolling Bearing under Variable Conditions Based on Image Recognition
Joint Authors
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-08-29
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Zhou, Bo& Cheng, Yujie. 2016. Fault Diagnosis for Rolling Bearing under Variable Conditions Based on Image Recognition. Shock and Vibration،Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1118835
Modern Language Association (MLA)
Zhou, Bo& Cheng, Yujie. Fault Diagnosis for Rolling Bearing under Variable Conditions Based on Image Recognition. Shock and Vibration No. 2016 (2016), pp.1-14.
https://search.emarefa.net/detail/BIM-1118835
American Medical Association (AMA)
Zhou, Bo& Cheng, Yujie. Fault Diagnosis for Rolling Bearing under Variable Conditions Based on Image Recognition. Shock and Vibration. 2016. Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1118835
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1118835