Fractional Dimensionless Indicator with Random Forest for Bearing Fault Diagnosis under Variable Speed Conditions
Fault diagnosis of rolling bearings under variable speed is a common issue in engineering practice, but it lacks an effective diagnosis algorithm, while approaches developed for steady speed cannot be directly applied. Therefore, for effectively identifying bearing faults under variable speed, this...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2022/1781340 |
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| _version_ | 1832565381383847936 |
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| author | Yujing Huang Zhi Xu Liang Cao Hao Hu Gang Tang |
| author_facet | Yujing Huang Zhi Xu Liang Cao Hao Hu Gang Tang |
| author_sort | Yujing Huang |
| collection | DOAJ |
| description | Fault diagnosis of rolling bearings under variable speed is a common issue in engineering practice, but it lacks an effective diagnosis algorithm, while approaches developed for steady speed cannot be directly applied. Therefore, for effectively identifying bearing faults under variable speed, this paper proposed a multiscale fractional dimensionless indicator (MSFDI) and put forward a fault diagnosis method with random forest (RF). It can overcome the feature space aliasing problem of traditional dimensionless indicators, which will lead to increased diagnosis uncertainty. The multiorder fractional Fourier transform is carried out on bearing signals to get a series of fractional Fourier domain components, which will be used to construct the original MSFDI feature set. Moreover, reliefF selects the sensitive MSFDIs as the input of the RF algorithm to determine the health condition. The effectiveness of the proposed method is verified by experiments and case studies. |
| format | Article |
| id | doaj-art-5c85933211964019bce8f1c5e9c9f978 |
| institution | Kabale University |
| issn | 1875-9203 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-5c85933211964019bce8f1c5e9c9f9782025-02-03T01:07:56ZengWileyShock and Vibration1875-92032022-01-01202210.1155/2022/1781340Fractional Dimensionless Indicator with Random Forest for Bearing Fault Diagnosis under Variable Speed ConditionsYujing Huang0Zhi Xu1Liang Cao2Hao Hu3Gang Tang4College of Mechanical and Electrical EngineeringAviation Key Laboratory of Science and Technology on Fault Diagnosis and Health ManagementAviation Key Laboratory of Science and Technology on Fault Diagnosis and Health ManagementCollege of Mechanical and Electrical EngineeringCollege of Mechanical and Electrical EngineeringFault diagnosis of rolling bearings under variable speed is a common issue in engineering practice, but it lacks an effective diagnosis algorithm, while approaches developed for steady speed cannot be directly applied. Therefore, for effectively identifying bearing faults under variable speed, this paper proposed a multiscale fractional dimensionless indicator (MSFDI) and put forward a fault diagnosis method with random forest (RF). It can overcome the feature space aliasing problem of traditional dimensionless indicators, which will lead to increased diagnosis uncertainty. The multiorder fractional Fourier transform is carried out on bearing signals to get a series of fractional Fourier domain components, which will be used to construct the original MSFDI feature set. Moreover, reliefF selects the sensitive MSFDIs as the input of the RF algorithm to determine the health condition. The effectiveness of the proposed method is verified by experiments and case studies.http://dx.doi.org/10.1155/2022/1781340 |
| spellingShingle | Yujing Huang Zhi Xu Liang Cao Hao Hu Gang Tang Fractional Dimensionless Indicator with Random Forest for Bearing Fault Diagnosis under Variable Speed Conditions Shock and Vibration |
| title | Fractional Dimensionless Indicator with Random Forest for Bearing Fault Diagnosis under Variable Speed Conditions |
| title_full | Fractional Dimensionless Indicator with Random Forest for Bearing Fault Diagnosis under Variable Speed Conditions |
| title_fullStr | Fractional Dimensionless Indicator with Random Forest for Bearing Fault Diagnosis under Variable Speed Conditions |
| title_full_unstemmed | Fractional Dimensionless Indicator with Random Forest for Bearing Fault Diagnosis under Variable Speed Conditions |
| title_short | Fractional Dimensionless Indicator with Random Forest for Bearing Fault Diagnosis under Variable Speed Conditions |
| title_sort | fractional dimensionless indicator with random forest for bearing fault diagnosis under variable speed conditions |
| url | http://dx.doi.org/10.1155/2022/1781340 |
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