An Accurate Integral Method for Vibration Signal Based on Feature Information Extraction
After summarizing the advantages and disadvantages of current integral methods, a novel vibration signal integral method based on feature information extraction was proposed. This method took full advantage of the self-adaptive filter characteristic and waveform correction feature of ensemble empiri...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2015/962793 |
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| _version_ | 1832562940239151104 |
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| author | Yong Zhu Wanlu Jiang Xiangdong Kong Zhi Zheng Haosong Hu |
| author_facet | Yong Zhu Wanlu Jiang Xiangdong Kong Zhi Zheng Haosong Hu |
| author_sort | Yong Zhu |
| collection | DOAJ |
| description | After summarizing the advantages and disadvantages of current integral methods, a novel vibration signal integral method based on feature information extraction was proposed. This method took full advantage of the self-adaptive filter characteristic and waveform correction feature of ensemble empirical mode decomposition in dealing with nonlinear and nonstationary signals. This research merged the superiorities of kurtosis, mean square error, energy, and singular value decomposition on signal feature extraction. The values of the four indexes aforementioned were combined into a feature vector. Then, the connotative characteristic components in vibration signal were accurately extracted by Euclidean distance search, and the desired integral signals were precisely reconstructed. With this method, the interference problem of invalid signal such as trend item and noise which plague traditional methods is commendably solved. The great cumulative error from the traditional time-domain integral is effectively overcome. Moreover, the large low-frequency error from the traditional frequency-domain integral is successfully avoided. Comparing with the traditional integral methods, this method is outstanding at removing noise and retaining useful feature information and shows higher accuracy and superiority. |
| format | Article |
| id | doaj-art-419e1eef23a1466e83cc78637416c482 |
| 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-419e1eef23a1466e83cc78637416c4822025-02-03T01:21:25ZengWileyShock and Vibration1070-96221875-92032015-01-01201510.1155/2015/962793962793An Accurate Integral Method for Vibration Signal Based on Feature Information ExtractionYong Zhu0Wanlu Jiang1Xiangdong Kong2Zhi Zheng3Haosong Hu4Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao, Hebei 066004, ChinaHebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao, Hebei 066004, ChinaHebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao, Hebei 066004, ChinaHebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao, Hebei 066004, ChinaKey Laboratory of Advanced Forging & Stamping Technology and Science, Yanshan University, Ministry of Education of China, Qinhuangdao, Hebei 066004, ChinaAfter summarizing the advantages and disadvantages of current integral methods, a novel vibration signal integral method based on feature information extraction was proposed. This method took full advantage of the self-adaptive filter characteristic and waveform correction feature of ensemble empirical mode decomposition in dealing with nonlinear and nonstationary signals. This research merged the superiorities of kurtosis, mean square error, energy, and singular value decomposition on signal feature extraction. The values of the four indexes aforementioned were combined into a feature vector. Then, the connotative characteristic components in vibration signal were accurately extracted by Euclidean distance search, and the desired integral signals were precisely reconstructed. With this method, the interference problem of invalid signal such as trend item and noise which plague traditional methods is commendably solved. The great cumulative error from the traditional time-domain integral is effectively overcome. Moreover, the large low-frequency error from the traditional frequency-domain integral is successfully avoided. Comparing with the traditional integral methods, this method is outstanding at removing noise and retaining useful feature information and shows higher accuracy and superiority.http://dx.doi.org/10.1155/2015/962793 |
| spellingShingle | Yong Zhu Wanlu Jiang Xiangdong Kong Zhi Zheng Haosong Hu An Accurate Integral Method for Vibration Signal Based on Feature Information Extraction Shock and Vibration |
| title | An Accurate Integral Method for Vibration Signal Based on Feature Information Extraction |
| title_full | An Accurate Integral Method for Vibration Signal Based on Feature Information Extraction |
| title_fullStr | An Accurate Integral Method for Vibration Signal Based on Feature Information Extraction |
| title_full_unstemmed | An Accurate Integral Method for Vibration Signal Based on Feature Information Extraction |
| title_short | An Accurate Integral Method for Vibration Signal Based on Feature Information Extraction |
| title_sort | accurate integral method for vibration signal based on feature information extraction |
| url | http://dx.doi.org/10.1155/2015/962793 |
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