FAULT DIAGNOSIS BASED ON SEMI-SUPERVISED LLTSA FOR DIMENSION REDUCTION
Linear local tangent space alignment（ LLTSA） is an unsupervised dimension reduction method,which will lends to remaining overlaps between faults when it is used to high-dimension fault feature for dimension reduction due to its incapacity of using part sample class label information. Aiming at this...
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Strength
2017-01-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2017.02.007 |
| _version_ | 1797767866616905728 |
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| author | LI Lei PANG Hai ZHANG QianTu |
| author_facet | LI Lei PANG Hai ZHANG QianTu |
| author_sort | LI Lei |
| collection | DOAJ |
| description | Linear local tangent space alignment（ LLTSA） is an unsupervised dimension reduction method,which will lends to remaining overlaps between faults when it is used to high-dimension fault feature for dimension reduction due to its incapacity of using part sample class label information. Aiming at this problem,semi-supervised linear local tangent space alignment（ SSLLTSA） dimension reduction method is proposed in this paper. In SS-LLTSA,the distance between different points is adjusted by utilizing part class label information,thereby a new distance matrix is formed and the neighborhood is construct through this new distance matrix. The improved method realized the combination of data intrinsic manifold structure and class label information,and more discriminative low-dimension features can been obtained. And then,the corresponding relationship between low-dimension feature and fault classes are established by using support vector machine（ SVM）. Dimension reduction with SS-LLTSA can effectively increase the discrimination of fault feature,and furthermore,SVM can further improve fault diagnosis accuracy with its excellent pattern recognition capacity. Finally,the effectiveness of the proposed method was verified through the fault diagnosis experiment of rolling bearing. |
| first_indexed | 2024-03-12T20:46:08Z |
| format | Article |
| id | doaj.art-788ca8a38f0a4e32b1df071d4dae32e4 |
| institution | Directory Open Access Journal |
| issn | 1001-9669 |
| language | zho |
| last_indexed | 2024-03-12T20:46:08Z |
| publishDate | 2017-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj.art-788ca8a38f0a4e32b1df071d4dae32e42023-08-01T07:44:03ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692017-01-013927928430597928FAULT DIAGNOSIS BASED ON SEMI-SUPERVISED LLTSA FOR DIMENSION REDUCTIONLI LeiPANG HaiZHANG QianTuLinear local tangent space alignment（ LLTSA） is an unsupervised dimension reduction method,which will lends to remaining overlaps between faults when it is used to high-dimension fault feature for dimension reduction due to its incapacity of using part sample class label information. Aiming at this problem,semi-supervised linear local tangent space alignment（ SSLLTSA） dimension reduction method is proposed in this paper. In SS-LLTSA,the distance between different points is adjusted by utilizing part class label information,thereby a new distance matrix is formed and the neighborhood is construct through this new distance matrix. The improved method realized the combination of data intrinsic manifold structure and class label information,and more discriminative low-dimension features can been obtained. And then,the corresponding relationship between low-dimension feature and fault classes are established by using support vector machine（ SVM）. Dimension reduction with SS-LLTSA can effectively increase the discrimination of fault feature,and furthermore,SVM can further improve fault diagnosis accuracy with its excellent pattern recognition capacity. Finally,the effectiveness of the proposed method was verified through the fault diagnosis experiment of rolling bearing.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2017.02.007Fault diagnosis;Dimension reduction;Semi-supervised linear local tangent space alignment（SSLLTSA）;Support vector machine（SVM） |
| spellingShingle | LI Lei PANG Hai ZHANG QianTu FAULT DIAGNOSIS BASED ON SEMI-SUPERVISED LLTSA FOR DIMENSION REDUCTION Jixie qiangdu Fault diagnosis;Dimension reduction;Semi-supervised linear local tangent space alignment（SSLLTSA）;Support vector machine（SVM） |
| title | FAULT DIAGNOSIS BASED ON SEMI-SUPERVISED LLTSA FOR DIMENSION REDUCTION |
| title_full | FAULT DIAGNOSIS BASED ON SEMI-SUPERVISED LLTSA FOR DIMENSION REDUCTION |
| title_fullStr | FAULT DIAGNOSIS BASED ON SEMI-SUPERVISED LLTSA FOR DIMENSION REDUCTION |
| title_full_unstemmed | FAULT DIAGNOSIS BASED ON SEMI-SUPERVISED LLTSA FOR DIMENSION REDUCTION |
| title_short | FAULT DIAGNOSIS BASED ON SEMI-SUPERVISED LLTSA FOR DIMENSION REDUCTION |
| title_sort | fault diagnosis based on semi supervised lltsa for dimension reduction |
| topic | Fault diagnosis;Dimension reduction;Semi-supervised linear local tangent space alignment（SSLLTSA）;Support vector machine（SVM） |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2017.02.007 |
| work_keys_str_mv | AT lilei faultdiagnosisbasedonsemisupervisedlltsafordimensionreduction AT panghai faultdiagnosisbasedonsemisupervisedlltsafordimensionreduction AT zhangqiantu faultdiagnosisbasedonsemisupervisedlltsafordimensionreduction |