Fault Identification based on Improved Supervised Orthogonal Neighborhood Preserving Embedding
The Orthogonal neighborhood preserving embedding（ ONPE） is an unsupervised feature dimension reduction method and only use global neighborhood parameter,when it is used to high- dimension fault feature for feature dimension reduction,it is incapacity of using sample class label information and adapt...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Transmission
2017-01-01
|
| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2017.01.004 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841547482050330624 |
|---|---|
| author | Ji Yunfeng Feng Liyuan Kuang Liang |
| author_facet | Ji Yunfeng Feng Liyuan Kuang Liang |
| author_sort | Ji Yunfeng |
| collection | DOAJ |
| description | The Orthogonal neighborhood preserving embedding（ ONPE） is an unsupervised feature dimension reduction method and only use global neighborhood parameter,when it is used to high- dimension fault feature for feature dimension reduction,it is incapacity of using sample class label information and adaptive adjust neighborhood parameter while the space distribution of samples changed. Aiming at the problems above,a fault identification method based on improved supervised orthogonal neighborhood preserving embedding（ IS-ONPE） for feature dimension reduction is proposed. In IS- ONPE,the distance between different points is adjusted by utilizing class label information,thereby a new distance matrix is formed and the neighborhood is constructed through this new distance matrix,at the same time,the neighborhood parameter are adaptive adjusted according to local cluster coefficient. With the low- dimensional feature as inputs of support vector machine（ SVM） for identifying fault types. The experiment results of gear fault identification indicate that the proposed method can identification gear fault in high accuracy,it has some superiority. |
| format | Article |
| id | doaj-art-54f4819159934bb58ae67ebfe7e48545 |
| institution | Kabale University |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2017-01-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-54f4819159934bb58ae67ebfe7e485452025-01-10T14:38:47ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392017-01-0141161929927935Fault Identification based on Improved Supervised Orthogonal Neighborhood Preserving EmbeddingJi YunfengFeng LiyuanKuang LiangThe Orthogonal neighborhood preserving embedding（ ONPE） is an unsupervised feature dimension reduction method and only use global neighborhood parameter,when it is used to high- dimension fault feature for feature dimension reduction,it is incapacity of using sample class label information and adaptive adjust neighborhood parameter while the space distribution of samples changed. Aiming at the problems above,a fault identification method based on improved supervised orthogonal neighborhood preserving embedding（ IS-ONPE） for feature dimension reduction is proposed. In IS- ONPE,the distance between different points is adjusted by utilizing class label information,thereby a new distance matrix is formed and the neighborhood is constructed through this new distance matrix,at the same time,the neighborhood parameter are adaptive adjusted according to local cluster coefficient. With the low- dimensional feature as inputs of support vector machine（ SVM） for identifying fault types. The experiment results of gear fault identification indicate that the proposed method can identification gear fault in high accuracy,it has some superiority.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2017.01.004Fault identificationFeature dimension reductionImproved supervised orthogonal neighborhood preserving embeddingGear |
| spellingShingle | Ji Yunfeng Feng Liyuan Kuang Liang Fault Identification based on Improved Supervised Orthogonal Neighborhood Preserving Embedding Jixie chuandong Fault identification Feature dimension reduction Improved supervised orthogonal neighborhood preserving embedding Gear |
| title | Fault Identification based on Improved Supervised Orthogonal Neighborhood Preserving Embedding |
| title_full | Fault Identification based on Improved Supervised Orthogonal Neighborhood Preserving Embedding |
| title_fullStr | Fault Identification based on Improved Supervised Orthogonal Neighborhood Preserving Embedding |
| title_full_unstemmed | Fault Identification based on Improved Supervised Orthogonal Neighborhood Preserving Embedding |
| title_short | Fault Identification based on Improved Supervised Orthogonal Neighborhood Preserving Embedding |
| title_sort | fault identification based on improved supervised orthogonal neighborhood preserving embedding |
| topic | Fault identification Feature dimension reduction Improved supervised orthogonal neighborhood preserving embedding Gear |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2017.01.004 |
| work_keys_str_mv | AT jiyunfeng faultidentificationbasedonimprovedsupervisedorthogonalneighborhoodpreservingembedding AT fengliyuan faultidentificationbasedonimprovedsupervisedorthogonalneighborhoodpreservingembedding AT kuangliang faultidentificationbasedonimprovedsupervisedorthogonalneighborhoodpreservingembedding |