A Hybrid Domain Degradation Feature Extraction Method for Motor Bearing Based on Distance Evaluation Technique
The vibration signal of the motor bearing has strong nonstationary and nonlinear characteristics, and it is arduous to accurately recognize the degradation state of the motor bearing with traditional single time or frequency domain indexes. A hybrid domain feature extraction method based on distance...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | International Journal of Rotating Machinery |
| Online Access: | http://dx.doi.org/10.1155/2017/2607254 |
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| _version_ | 1832561430237282304 |
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| author | Baiyan Chen Hongru Li He Yu Yukui Wang |
| author_facet | Baiyan Chen Hongru Li He Yu Yukui Wang |
| author_sort | Baiyan Chen |
| collection | DOAJ |
| description | The vibration signal of the motor bearing has strong nonstationary and nonlinear characteristics, and it is arduous to accurately recognize the degradation state of the motor bearing with traditional single time or frequency domain indexes. A hybrid domain feature extraction method based on distance evaluation technique (DET) is proposed to solve this problem. Firstly, the vibration signal of the motor bearing is decomposed by ensemble empirical mode decomposition (EEMD). The proper intrinsic mode function (IMF) component that is the most sensitive to the degradation of the motor bearing is selected according to the sensitive IMF selection algorithm based on the similarity evaluation. Then the distance evaluation factor of each characteristic parameter is calculated by the DET method. The differential method is used to extract sensitive characteristic parameters which compose the characteristic matrix. And then the extracted degradation characteristic matrix is used as the input of support vector machine (SVM) to identify the degradation state. Finally, It is demonstrated that the proposed hybrid domain feature extraction method has higher recognition accuracy and shorter recognition time by comparative analysis. The positive performance of the method is verified. |
| format | Article |
| id | doaj-art-648f9da0363242ed9e38774d7fea727f |
| institution | Kabale University |
| issn | 1023-621X 1542-3034 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Rotating Machinery |
| spelling | doaj-art-648f9da0363242ed9e38774d7fea727f2025-02-03T01:25:07ZengWileyInternational Journal of Rotating Machinery1023-621X1542-30342017-01-01201710.1155/2017/26072542607254A Hybrid Domain Degradation Feature Extraction Method for Motor Bearing Based on Distance Evaluation TechniqueBaiyan Chen0Hongru Li1He Yu2Yukui Wang3Mechanical Engineering College, Shijiazhuang 050003, ChinaMechanical Engineering College, Shijiazhuang 050003, ChinaMechanical Engineering College, Shijiazhuang 050003, ChinaAir Force Logistics College of PLA, Xuzhou 221000, ChinaThe vibration signal of the motor bearing has strong nonstationary and nonlinear characteristics, and it is arduous to accurately recognize the degradation state of the motor bearing with traditional single time or frequency domain indexes. A hybrid domain feature extraction method based on distance evaluation technique (DET) is proposed to solve this problem. Firstly, the vibration signal of the motor bearing is decomposed by ensemble empirical mode decomposition (EEMD). The proper intrinsic mode function (IMF) component that is the most sensitive to the degradation of the motor bearing is selected according to the sensitive IMF selection algorithm based on the similarity evaluation. Then the distance evaluation factor of each characteristic parameter is calculated by the DET method. The differential method is used to extract sensitive characteristic parameters which compose the characteristic matrix. And then the extracted degradation characteristic matrix is used as the input of support vector machine (SVM) to identify the degradation state. Finally, It is demonstrated that the proposed hybrid domain feature extraction method has higher recognition accuracy and shorter recognition time by comparative analysis. The positive performance of the method is verified.http://dx.doi.org/10.1155/2017/2607254 |
| spellingShingle | Baiyan Chen Hongru Li He Yu Yukui Wang A Hybrid Domain Degradation Feature Extraction Method for Motor Bearing Based on Distance Evaluation Technique International Journal of Rotating Machinery |
| title | A Hybrid Domain Degradation Feature Extraction Method for Motor Bearing Based on Distance Evaluation Technique |
| title_full | A Hybrid Domain Degradation Feature Extraction Method for Motor Bearing Based on Distance Evaluation Technique |
| title_fullStr | A Hybrid Domain Degradation Feature Extraction Method for Motor Bearing Based on Distance Evaluation Technique |
| title_full_unstemmed | A Hybrid Domain Degradation Feature Extraction Method for Motor Bearing Based on Distance Evaluation Technique |
| title_short | A Hybrid Domain Degradation Feature Extraction Method for Motor Bearing Based on Distance Evaluation Technique |
| title_sort | hybrid domain degradation feature extraction method for motor bearing based on distance evaluation technique |
| url | http://dx.doi.org/10.1155/2017/2607254 |
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