BearingCog: A Bearing Fault Diagnosis Method under Variable Operational Conditions
Rolling bearing is a pivotal component for rotating equipment, which has high failure rates. Bearing failure can cause the equipment to lose control or even casualties, resulting in significant economic losses. This article diagnoses the bearings in variable operational conditions. A novel fault dia...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-05-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/10/5240 |
| _version_ | 1797501737292005376 |
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| author | Lei Fu Zepeng Ma Debin Wu Jia Liu Fang Xu Qi Zhong Tiantian Zhu |
| author_facet | Lei Fu Zepeng Ma Debin Wu Jia Liu Fang Xu Qi Zhong Tiantian Zhu |
| author_sort | Lei Fu |
| collection | DOAJ |
| description | Rolling bearing is a pivotal component for rotating equipment, which has high failure rates. Bearing failure can cause the equipment to lose control or even casualties, resulting in significant economic losses. This article diagnoses the bearings in variable operational conditions. A novel fault diagnosis framework is proposed to improve the efficiency of fault classification. The variational modal decomposition (VMD) is first utilized to expand the features of the fault signal. Then, principal component analysis (PCA) selects the most representative fault features from the VMD results. After that, the multi-information fusion data is applied to improve the classification accuracy of the support vector machine (SVM). The comparison with respect to some traditional classification methods is illustrated in detail. The diagnostic results show that the proposed framework is a validated tool for diagnosing the bearings. |
| first_indexed | 2024-03-10T03:22:51Z |
| format | Article |
| id | doaj.art-dacfcffdc1be4f1091617aff48e2298f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T03:22:51Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-dacfcffdc1be4f1091617aff48e2298f2023-11-23T09:59:44ZengMDPI AGApplied Sciences2076-34172022-05-011210524010.3390/app12105240BearingCog: A Bearing Fault Diagnosis Method under Variable Operational ConditionsLei Fu0Zepeng Ma1Debin Wu2Jia Liu3Fang Xu4Qi Zhong5Tiantian Zhu6College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, ChinaCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, ChinaCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, ChinaCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, ChinaCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, ChinaCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, ChinaCollege of Computer Science & Technology, Zhejiang University of Technology, Hangzhou 310023, ChinaRolling bearing is a pivotal component for rotating equipment, which has high failure rates. Bearing failure can cause the equipment to lose control or even casualties, resulting in significant economic losses. This article diagnoses the bearings in variable operational conditions. A novel fault diagnosis framework is proposed to improve the efficiency of fault classification. The variational modal decomposition (VMD) is first utilized to expand the features of the fault signal. Then, principal component analysis (PCA) selects the most representative fault features from the VMD results. After that, the multi-information fusion data is applied to improve the classification accuracy of the support vector machine (SVM). The comparison with respect to some traditional classification methods is illustrated in detail. The diagnostic results show that the proposed framework is a validated tool for diagnosing the bearings.https://www.mdpi.com/2076-3417/12/10/5240fault diagnosisvariable conditionsfeature extractionmulti-information fusionsupport vector machine |
| spellingShingle | Lei Fu Zepeng Ma Debin Wu Jia Liu Fang Xu Qi Zhong Tiantian Zhu BearingCog: A Bearing Fault Diagnosis Method under Variable Operational Conditions Applied Sciences fault diagnosis variable conditions feature extraction multi-information fusion support vector machine |
| title | BearingCog: A Bearing Fault Diagnosis Method under Variable Operational Conditions |
| title_full | BearingCog: A Bearing Fault Diagnosis Method under Variable Operational Conditions |
| title_fullStr | BearingCog: A Bearing Fault Diagnosis Method under Variable Operational Conditions |
| title_full_unstemmed | BearingCog: A Bearing Fault Diagnosis Method under Variable Operational Conditions |
| title_short | BearingCog: A Bearing Fault Diagnosis Method under Variable Operational Conditions |
| title_sort | bearingcog a bearing fault diagnosis method under variable operational conditions |
| topic | fault diagnosis variable conditions feature extraction multi-information fusion support vector machine |
| url | https://www.mdpi.com/2076-3417/12/10/5240 |
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