APPLICATION OF SEMI SUPERVISED LAPLACE SCORE IN ROLLING BEARING FAULT DIAGNOSIS (MT)
Aiming at the problem of insufficient labeled samples in the process of rolling bearing fault diagnosis, a rolling bearing fault diagnosis model based on semi supervised Laplace score（SSLS） and kernel principal component analysis（KPCA） is proposed by combining with the idea of feature selection and...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2023-01-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2023.04.002 |
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| _version_ | 1841534322765463552 |
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| author | LIANG Chuang CHEN ChangZheng LIU Ye JIA XinYing |
| author_facet | LIANG Chuang CHEN ChangZheng LIU Ye JIA XinYing |
| author_sort | LIANG Chuang |
| collection | DOAJ |
| description | Aiming at the problem of insufficient labeled samples in the process of rolling bearing fault diagnosis, a rolling bearing fault diagnosis model based on semi supervised Laplace score（SSLS） and kernel principal component analysis（KPCA） is proposed by combining with the idea of feature selection and secondary mining. SSLS applies the semi supervised idea to the Laplace score feature selection method, uses a small number of labeled samples and a large number of unlabeled samples, and combines KPCA to excavate fault features for a second time. At the same time, particle swarm optimization-based support vector machine（PSO-SVM） algorithm is used for fault classification. Finally, the model is applied to the process of experimental data analysis. The results show that the model can not only reduce the workload of sample marking, but also maintain a high accuracy in rolling bearing fault classification, which verifies the effectiveness and engineering practicability of the model. |
| format | Article |
| id | doaj-art-5b6fd14189c348ada97bb2888621dde1 |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2023-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-5b6fd14189c348ada97bb2888621dde12025-01-15T02:40:53ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692023-01-0177177742274557APPLICATION OF SEMI SUPERVISED LAPLACE SCORE IN ROLLING BEARING FAULT DIAGNOSIS (MT)LIANG ChuangCHEN ChangZhengLIU YeJIA XinYingAiming at the problem of insufficient labeled samples in the process of rolling bearing fault diagnosis, a rolling bearing fault diagnosis model based on semi supervised Laplace score（SSLS） and kernel principal component analysis（KPCA） is proposed by combining with the idea of feature selection and secondary mining. SSLS applies the semi supervised idea to the Laplace score feature selection method, uses a small number of labeled samples and a large number of unlabeled samples, and combines KPCA to excavate fault features for a second time. At the same time, particle swarm optimization-based support vector machine（PSO-SVM） algorithm is used for fault classification. Finally, the model is applied to the process of experimental data analysis. The results show that the model can not only reduce the workload of sample marking, but also maintain a high accuracy in rolling bearing fault classification, which verifies the effectiveness and engineering practicability of the model.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2023.04.002Feature selectionSemi supervised Laplace scoreKernel principal component analysisParticle swarm optimization-based support vector machineFault diagnosis |
| spellingShingle | LIANG Chuang CHEN ChangZheng LIU Ye JIA XinYing APPLICATION OF SEMI SUPERVISED LAPLACE SCORE IN ROLLING BEARING FAULT DIAGNOSIS (MT) Jixie qiangdu Feature selection Semi supervised Laplace score Kernel principal component analysis Particle swarm optimization-based support vector machine Fault diagnosis |
| title | APPLICATION OF SEMI SUPERVISED LAPLACE SCORE IN ROLLING BEARING FAULT DIAGNOSIS (MT) |
| title_full | APPLICATION OF SEMI SUPERVISED LAPLACE SCORE IN ROLLING BEARING FAULT DIAGNOSIS (MT) |
| title_fullStr | APPLICATION OF SEMI SUPERVISED LAPLACE SCORE IN ROLLING BEARING FAULT DIAGNOSIS (MT) |
| title_full_unstemmed | APPLICATION OF SEMI SUPERVISED LAPLACE SCORE IN ROLLING BEARING FAULT DIAGNOSIS (MT) |
| title_short | APPLICATION OF SEMI SUPERVISED LAPLACE SCORE IN ROLLING BEARING FAULT DIAGNOSIS (MT) |
| title_sort | application of semi supervised laplace score in rolling bearing fault diagnosis mt |
| topic | Feature selection Semi supervised Laplace score Kernel principal component analysis Particle swarm optimization-based support vector machine Fault diagnosis |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2023.04.002 |
| work_keys_str_mv | AT liangchuang applicationofsemisupervisedlaplacescoreinrollingbearingfaultdiagnosismt AT chenchangzheng applicationofsemisupervisedlaplacescoreinrollingbearingfaultdiagnosismt AT liuye applicationofsemisupervisedlaplacescoreinrollingbearingfaultdiagnosismt AT jiaxinying applicationofsemisupervisedlaplacescoreinrollingbearingfaultdiagnosismt |