基于特征评估与核主分量分析的齿轮故障分类方法
For the blindness and one-sidedness of selection and fusion of mechanical fault features without priori knowledge,a novel method of gear fault feature extraction and classification based on feature evaluation and kernel principal component analysis is presented,where the original signals are decompo...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Transmission
2014-01-01
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| Series: | Jixie chuandong |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2014.11.019 |
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| author | 瞿雷 戴光昊 王琇峰 沈玉娣 |
| author_facet | 瞿雷 戴光昊 王琇峰 沈玉娣 |
| author_sort | 瞿雷 |
| collection | DOAJ |
| description | For the blindness and one-sidedness of selection and fusion of mechanical fault features without priori knowledge,a novel method of gear fault feature extraction and classification based on feature evaluation and kernel principal component analysis is presented,where the original signals are decomposed with wavelet pocket decomposition（WPD）,and the features in time domain are extracted from the original signals and each decomposed signal to compose the combined features.Furthermore,the threshold value for stability and the filtering scale factor for sensitivity are confirmed to evaluate the features by the combined method with stability and sensitivity,and the nonlinear features are extracted from the residual features by using the method of kernel principal component analysis（KPCA）to realize the classification of different fault conditions.The experimental results of gearbox demonstrate that the method integrating WPD,combined feature evaluation method and KPCA,could better extract the feature information of gear fault,remove the unstable and insensitive ones from a large number of features,and obviously improve the result of nonlinear feature extraction of gear fault for KPCA. |
| format | Article |
| id | doaj-art-bec2b89542cc4dcea0f2aa93c24beffe |
| institution | OA Journals |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2014-01-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-bec2b89542cc4dcea0f2aa93c24beffe2025-08-20T01:52:49ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392014-01-013810511088642669基于特征评估与核主分量分析的齿轮故障分类方法瞿雷戴光昊王琇峰沈玉娣For the blindness and one-sidedness of selection and fusion of mechanical fault features without priori knowledge,a novel method of gear fault feature extraction and classification based on feature evaluation and kernel principal component analysis is presented,where the original signals are decomposed with wavelet pocket decomposition（WPD）,and the features in time domain are extracted from the original signals and each decomposed signal to compose the combined features.Furthermore,the threshold value for stability and the filtering scale factor for sensitivity are confirmed to evaluate the features by the combined method with stability and sensitivity,and the nonlinear features are extracted from the residual features by using the method of kernel principal component analysis（KPCA）to realize the classification of different fault conditions.The experimental results of gearbox demonstrate that the method integrating WPD,combined feature evaluation method and KPCA,could better extract the feature information of gear fault,remove the unstable and insensitive ones from a large number of features,and obviously improve the result of nonlinear feature extraction of gear fault for KPCA.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2014.11.019 |
| spellingShingle | 瞿雷 戴光昊 王琇峰 沈玉娣 基于特征评估与核主分量分析的齿轮故障分类方法 Jixie chuandong |
| title | 基于特征评估与核主分量分析的齿轮故障分类方法 |
| title_full | 基于特征评估与核主分量分析的齿轮故障分类方法 |
| title_fullStr | 基于特征评估与核主分量分析的齿轮故障分类方法 |
| title_full_unstemmed | 基于特征评估与核主分量分析的齿轮故障分类方法 |
| title_short | 基于特征评估与核主分量分析的齿轮故障分类方法 |
| title_sort | 基于特征评估与核主分量分析的齿轮故障分类方法 |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2014.11.019 |
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