A Fault Diagnosis Method for Rolling Bearings Based on Feature Fusion of Multifractal Detrended Fluctuation Analysis and Alpha Stable Distribution
Joint Authors
Xiong, Qing
Zhang, Weihua
Lu, Tianwei
Mei, Guiming
Liang, Shulin
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-12-24
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
When rolling bearings fail, it is usually difficult to determine the degree of damage.
To address this problem, a new fault diagnosis method was developed to perform feature extraction and intelligent classification of various fault position and damage degree of rolling bearing signals.
Firstly, Multifractal Detrended Fluctuation Analysis (MFDFA) was used to compute five MFDFA features while five Alpha Stable Distribution (ASD) features were obtained by fitting the distribution to the vibration signals of each status and calculating the Probability Density Function (PDF).
Secondly, Kernel Principle Component Analysis (KPCA) was used to achieve dimensionality reduction fusion of the combination of original features to gain the Kernel Principle Component Fusion Features (KPCFFs).
Thirdly, the KPCFFs served as the input of Least Squares Support Vectors Machine (LSSVM) based on Particle Swarm Optimization (PSO) to assess rolling bearings’ fault position and damage severity.
Finally, the effectiveness of the method was validated by bench test data.
The results demonstrated that the developed method can achieve intelligent diagnosis of rolling bearings’ fault position and damage degree and can yield better diagnosis accuracy than single feature method or corresponding single feature fusion method.
American Psychological Association (APA)
Xiong, Qing& Zhang, Weihua& Lu, Tianwei& Mei, Guiming& Liang, Shulin. 2015. A Fault Diagnosis Method for Rolling Bearings Based on Feature Fusion of Multifractal Detrended Fluctuation Analysis and Alpha Stable Distribution. Shock and Vibration،Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1118783
Modern Language Association (MLA)
Xiong, Qing…[et al.]. A Fault Diagnosis Method for Rolling Bearings Based on Feature Fusion of Multifractal Detrended Fluctuation Analysis and Alpha Stable Distribution. Shock and Vibration No. 2016 (2016), pp.1-12.
https://search.emarefa.net/detail/BIM-1118783
American Medical Association (AMA)
Xiong, Qing& Zhang, Weihua& Lu, Tianwei& Mei, Guiming& Liang, Shulin. A Fault Diagnosis Method for Rolling Bearings Based on Feature Fusion of Multifractal Detrended Fluctuation Analysis and Alpha Stable Distribution. Shock and Vibration. 2015. Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1118783
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1118783