The Hybrid KICA-GDA-LSSVM Method Research on Rolling Bearing Fault Feature Extraction and Classification
Rolling element bearings are widely used in high-speed rotating machinery; thus proper monitoring and fault diagnosis procedure to avoid major machine failures is necessary. As feature extraction and classification based on vibration signals are important in condition monitoring technique, and super...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2015/512163 |
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| _version_ | 1832556440885133312 |
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| author | Jiyong Li Shunming Li Xiaohong Chen Lili Wang |
| author_facet | Jiyong Li Shunming Li Xiaohong Chen Lili Wang |
| author_sort | Jiyong Li |
| collection | DOAJ |
| description | Rolling element bearings are widely used in high-speed rotating machinery; thus proper monitoring and fault diagnosis procedure to avoid major machine failures is necessary. As feature extraction and classification based on vibration signals are important in condition monitoring technique, and superfluous features may degrade the classification performance, it is needed to extract independent features, so LSSVM (least square support vector machine) based on hybrid KICA-GDA (kernel independent component analysis-generalized discriminate analysis) is presented in this study. A new method named sensitive subband feature set design (SSFD) based on wavelet packet is also presented; using proposed variance differential spectrum method, the sensitive subbands are selected. Firstly, independent features are obtained by KICA; the feature redundancy is reduced. Secondly, feature dimension is reduced by GDA. Finally, the projected feature is classified by LSSVM. The whole paper aims to classify the feature vectors extracted from the time series and magnitude of spectral analysis and to discriminate the state of the rolling element bearings by virtue of multiclass LSSVM. Experimental results from two different fault-seeded bearing tests show good performance of the proposed method. |
| format | Article |
| id | doaj-art-d7386d6a75934bf5b607312f4810be15 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-d7386d6a75934bf5b607312f4810be152025-02-03T05:45:25ZengWileyShock and Vibration1070-96221875-92032015-01-01201510.1155/2015/512163512163The Hybrid KICA-GDA-LSSVM Method Research on Rolling Bearing Fault Feature Extraction and ClassificationJiyong Li0Shunming Li1Xiaohong Chen2Lili Wang3College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Science, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaRolling element bearings are widely used in high-speed rotating machinery; thus proper monitoring and fault diagnosis procedure to avoid major machine failures is necessary. As feature extraction and classification based on vibration signals are important in condition monitoring technique, and superfluous features may degrade the classification performance, it is needed to extract independent features, so LSSVM (least square support vector machine) based on hybrid KICA-GDA (kernel independent component analysis-generalized discriminate analysis) is presented in this study. A new method named sensitive subband feature set design (SSFD) based on wavelet packet is also presented; using proposed variance differential spectrum method, the sensitive subbands are selected. Firstly, independent features are obtained by KICA; the feature redundancy is reduced. Secondly, feature dimension is reduced by GDA. Finally, the projected feature is classified by LSSVM. The whole paper aims to classify the feature vectors extracted from the time series and magnitude of spectral analysis and to discriminate the state of the rolling element bearings by virtue of multiclass LSSVM. Experimental results from two different fault-seeded bearing tests show good performance of the proposed method.http://dx.doi.org/10.1155/2015/512163 |
| spellingShingle | Jiyong Li Shunming Li Xiaohong Chen Lili Wang The Hybrid KICA-GDA-LSSVM Method Research on Rolling Bearing Fault Feature Extraction and Classification Shock and Vibration |
| title | The Hybrid KICA-GDA-LSSVM Method Research on Rolling Bearing Fault Feature Extraction and Classification |
| title_full | The Hybrid KICA-GDA-LSSVM Method Research on Rolling Bearing Fault Feature Extraction and Classification |
| title_fullStr | The Hybrid KICA-GDA-LSSVM Method Research on Rolling Bearing Fault Feature Extraction and Classification |
| title_full_unstemmed | The Hybrid KICA-GDA-LSSVM Method Research on Rolling Bearing Fault Feature Extraction and Classification |
| title_short | The Hybrid KICA-GDA-LSSVM Method Research on Rolling Bearing Fault Feature Extraction and Classification |
| title_sort | hybrid kica gda lssvm method research on rolling bearing fault feature extraction and classification |
| url | http://dx.doi.org/10.1155/2015/512163 |
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