Optimal SES Selection Based on SVD and Its Application to Incipient Bearing Fault Diagnosis
Rotating machinery has extensive industrial applications, and rolling element bearing (REB) is one of the core parts. To distinguish the incipient fault of bearing before it steps into serious failure is the main task of condition monitoring and fault diagnosis technology which could guarantee the r...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2018/8067416 |
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| _version_ | 1832568453248057344 |
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| author | Longlong Li Yahui Cui Runlin Chen Xiaolin Liu |
| author_facet | Longlong Li Yahui Cui Runlin Chen Xiaolin Liu |
| author_sort | Longlong Li |
| collection | DOAJ |
| description | Rotating machinery has extensive industrial applications, and rolling element bearing (REB) is one of the core parts. To distinguish the incipient fault of bearing before it steps into serious failure is the main task of condition monitoring and fault diagnosis technology which could guarantee the reliability and security of rotating machinery. The early defect occurring in the REB is too weak and manifests itself in heavy surrounding noise, thus leading to the inefficiency of the fault detection techniques. Aiming at the vibration signal purification and promoting the potential of defects detection, a new method is proposed in this paper based on the combination of singular value decomposition (SVD) technique and squared envelope spectrum (SES). The kurtosis of SES (KSES) is employed to select the optimal singular component (SC) obtained by applying SVD to vibration signal, which provides the information of the REB for fault diagnosis. Moreover, the rolling bearing accelerated life test with the bearing running from normal state to failure is adopted to evaluate the performance of the SVD-KSES, and results demonstrate the proposed approach can detect the incipient faults from vibration signal in the natural degradation process. |
| format | Article |
| id | doaj-art-afbb930d878a4f88a760ce8ff278b82d |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-afbb930d878a4f88a760ce8ff278b82d2025-02-03T00:59:08ZengWileyShock and Vibration1070-96221875-92032018-01-01201810.1155/2018/80674168067416Optimal SES Selection Based on SVD and Its Application to Incipient Bearing Fault DiagnosisLonglong Li0Yahui Cui1Runlin Chen2Xiaolin Liu3School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an, ChinaSchool of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an, ChinaSchool of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an, ChinaSchool of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an, ChinaRotating machinery has extensive industrial applications, and rolling element bearing (REB) is one of the core parts. To distinguish the incipient fault of bearing before it steps into serious failure is the main task of condition monitoring and fault diagnosis technology which could guarantee the reliability and security of rotating machinery. The early defect occurring in the REB is too weak and manifests itself in heavy surrounding noise, thus leading to the inefficiency of the fault detection techniques. Aiming at the vibration signal purification and promoting the potential of defects detection, a new method is proposed in this paper based on the combination of singular value decomposition (SVD) technique and squared envelope spectrum (SES). The kurtosis of SES (KSES) is employed to select the optimal singular component (SC) obtained by applying SVD to vibration signal, which provides the information of the REB for fault diagnosis. Moreover, the rolling bearing accelerated life test with the bearing running from normal state to failure is adopted to evaluate the performance of the SVD-KSES, and results demonstrate the proposed approach can detect the incipient faults from vibration signal in the natural degradation process.http://dx.doi.org/10.1155/2018/8067416 |
| spellingShingle | Longlong Li Yahui Cui Runlin Chen Xiaolin Liu Optimal SES Selection Based on SVD and Its Application to Incipient Bearing Fault Diagnosis Shock and Vibration |
| title | Optimal SES Selection Based on SVD and Its Application to Incipient Bearing Fault Diagnosis |
| title_full | Optimal SES Selection Based on SVD and Its Application to Incipient Bearing Fault Diagnosis |
| title_fullStr | Optimal SES Selection Based on SVD and Its Application to Incipient Bearing Fault Diagnosis |
| title_full_unstemmed | Optimal SES Selection Based on SVD and Its Application to Incipient Bearing Fault Diagnosis |
| title_short | Optimal SES Selection Based on SVD and Its Application to Incipient Bearing Fault Diagnosis |
| title_sort | optimal ses selection based on svd and its application to incipient bearing fault diagnosis |
| url | http://dx.doi.org/10.1155/2018/8067416 |
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