A Fault Diagnosis Method for Rolling Bearings Based on Feature Fusion of Multifractal Detrended Fluctuation Analysis and Alpha Stable Distribution
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, Mul...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2016/1232893 |
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| _version_ | 1832558870307799040 |
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| author | Qing Xiong Weihua Zhang Tianwei Lu Guiming Mei Shulin Liang |
| author_facet | Qing Xiong Weihua Zhang Tianwei Lu Guiming Mei Shulin Liang |
| author_sort | Qing Xiong |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-792e4d3728b2411fb86363ba816be2e6 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-792e4d3728b2411fb86363ba816be2e62025-02-03T01:31:24ZengWileyShock and Vibration1070-96221875-92032016-01-01201610.1155/2016/12328931232893A Fault Diagnosis Method for Rolling Bearings Based on Feature Fusion of Multifractal Detrended Fluctuation Analysis and Alpha Stable DistributionQing Xiong0Weihua Zhang1Tianwei Lu2Guiming Mei3Shulin Liang4State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, Sichuan 610031, ChinaState Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, Sichuan 610031, ChinaSchool of Mechanical Engineering, Southwest Jiaotong University, Chengdu, Sichuan 610031, ChinaState Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, Sichuan 610031, ChinaCNR Changchun Railway Vehicle Co., Changchun, Jilin 130062, ChinaWhen 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.http://dx.doi.org/10.1155/2016/1232893 |
| spellingShingle | Qing Xiong Weihua Zhang Tianwei Lu Guiming Mei Shulin Liang A Fault Diagnosis Method for Rolling Bearings Based on Feature Fusion of Multifractal Detrended Fluctuation Analysis and Alpha Stable Distribution Shock and Vibration |
| title | A Fault Diagnosis Method for Rolling Bearings Based on Feature Fusion of Multifractal Detrended Fluctuation Analysis and Alpha Stable Distribution |
| title_full | A Fault Diagnosis Method for Rolling Bearings Based on Feature Fusion of Multifractal Detrended Fluctuation Analysis and Alpha Stable Distribution |
| title_fullStr | A Fault Diagnosis Method for Rolling Bearings Based on Feature Fusion of Multifractal Detrended Fluctuation Analysis and Alpha Stable Distribution |
| title_full_unstemmed | A Fault Diagnosis Method for Rolling Bearings Based on Feature Fusion of Multifractal Detrended Fluctuation Analysis and Alpha Stable Distribution |
| title_short | A Fault Diagnosis Method for Rolling Bearings Based on Feature Fusion of Multifractal Detrended Fluctuation Analysis and Alpha Stable Distribution |
| title_sort | fault diagnosis method for rolling bearings based on feature fusion of multifractal detrended fluctuation analysis and alpha stable distribution |
| url | http://dx.doi.org/10.1155/2016/1232893 |
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