Study on the Rolling Bearing Fault Diagnosis based on the Hilbert Envelope Spectrum Singular Value and IPSO-SVM
For the problem that the characterization of the gear fault signal feature is difficult to extract and the structure parameters selection of support vector machine（ SVM） are based on experience leads the poor precision and generalization ability of fault state recognition,a method that IPSO- SVM rol...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Transmission
2017-01-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2017.03.033 |
| _version_ | 1826881819674411008 |
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| author | Qin Bo Sun Guodong Zhang Liqiang Liu Yongliang Zhang Chao Wang Jianguo |
| author_facet | Qin Bo Sun Guodong Zhang Liqiang Liu Yongliang Zhang Chao Wang Jianguo |
| author_sort | Qin Bo |
| collection | DOAJ |
| description | For the problem that the characterization of the gear fault signal feature is difficult to extract and the structure parameters selection of support vector machine（ SVM） are based on experience leads the poor precision and generalization ability of fault state recognition,a method that IPSO- SVM rolling bearing fault diagnosis based on the Hilbert envelope spectrum singular value is proposed. Firstly,the rolling bearing signal is divided by EMD,it selects IMFs that contains main characteristics of signal for Hilbert demodulation envelope analysis to obtain envelope matrix and the singular value decomposition is carried out. Secondly,the IPSO algorithm is used to optimize the penalty coefficient and the structural parameters of SVM to set up the rolling bearing fault classification model. And by using the bearing data of Case Western Reserve University,the validity of the method is verified. The experimental results show that IPSO- SVM rolling bearing fault diagnosis based on the Hilbert envelope spectrum singular value compared with the fault classification model based on BP,SVM has higher precision and stronger generalization ability. |
| first_indexed | 2024-03-13T09:21:25Z |
| format | Article |
| id | doaj.art-9ea4fdb81d3b45569046b5f7c931ec84 |
| institution | Directory Open Access Journal |
| issn | 1004-2539 |
| language | zho |
| last_indexed | 2025-02-17T02:54:38Z |
| publishDate | 2017-01-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj.art-9ea4fdb81d3b45569046b5f7c931ec842025-01-10T14:23:55ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392017-01-014116617129929005Study on the Rolling Bearing Fault Diagnosis based on the Hilbert Envelope Spectrum Singular Value and IPSO-SVMQin BoSun GuodongZhang LiqiangLiu YongliangZhang ChaoWang JianguoFor the problem that the characterization of the gear fault signal feature is difficult to extract and the structure parameters selection of support vector machine（ SVM） are based on experience leads the poor precision and generalization ability of fault state recognition,a method that IPSO- SVM rolling bearing fault diagnosis based on the Hilbert envelope spectrum singular value is proposed. Firstly,the rolling bearing signal is divided by EMD,it selects IMFs that contains main characteristics of signal for Hilbert demodulation envelope analysis to obtain envelope matrix and the singular value decomposition is carried out. Secondly,the IPSO algorithm is used to optimize the penalty coefficient and the structural parameters of SVM to set up the rolling bearing fault classification model. And by using the bearing data of Case Western Reserve University,the validity of the method is verified. The experimental results show that IPSO- SVM rolling bearing fault diagnosis based on the Hilbert envelope spectrum singular value compared with the fault classification model based on BP,SVM has higher precision and stronger generalization ability.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2017.03.033EMDIntrinsic mode functionPartical swarm optimizationSVMRolling bearing |
| spellingShingle | Qin Bo Sun Guodong Zhang Liqiang Liu Yongliang Zhang Chao Wang Jianguo Study on the Rolling Bearing Fault Diagnosis based on the Hilbert Envelope Spectrum Singular Value and IPSO-SVM Jixie chuandong EMD Intrinsic mode function Partical swarm optimization SVM Rolling bearing |
| title | Study on the Rolling Bearing Fault Diagnosis based on the Hilbert Envelope Spectrum Singular Value and IPSO-SVM |
| title_full | Study on the Rolling Bearing Fault Diagnosis based on the Hilbert Envelope Spectrum Singular Value and IPSO-SVM |
| title_fullStr | Study on the Rolling Bearing Fault Diagnosis based on the Hilbert Envelope Spectrum Singular Value and IPSO-SVM |
| title_full_unstemmed | Study on the Rolling Bearing Fault Diagnosis based on the Hilbert Envelope Spectrum Singular Value and IPSO-SVM |
| title_short | Study on the Rolling Bearing Fault Diagnosis based on the Hilbert Envelope Spectrum Singular Value and IPSO-SVM |
| title_sort | study on the rolling bearing fault diagnosis based on the hilbert envelope spectrum singular value and ipso svm |
| topic | EMD Intrinsic mode function Partical swarm optimization SVM Rolling bearing |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2017.03.033 |
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