Mechanical Fault Diagnosis based on LCD Information Entropy Feature and SVM
A new approach for mechanical fault diagnosis based on local characteristic- scale decomposition（ LCD） information entropy feature and support vector machine（ SVM） is proposed. Firstly,the fault mechanical vibration signal is decomposed by using the LCD to obtain a certain number of intrinsic scale...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Transmission
2015-01-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2015.12.031 |
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| _version_ | 1841548712805924864 |
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| author | Zhang Qiantu Fang Liqing Zhao Yulong Lv Yan |
| author_facet | Zhang Qiantu Fang Liqing Zhao Yulong Lv Yan |
| author_sort | Zhang Qiantu |
| collection | DOAJ |
| description | A new approach for mechanical fault diagnosis based on local characteristic- scale decomposition（ LCD） information entropy feature and support vector machine（ SVM） is proposed. Firstly,the fault mechanical vibration signal is decomposed by using the LCD to obtain a certain number of intrinsic scale component（ ISC）. Secondly,combined with information entropy theory,the singular spectrum entropy in time domain,power spectrum entropy in frequency domain,feature space entropy,marginal spectrum entropy and momentary energy entropy in time- frequency domain are defined and used as the feature vector. At last,the feature vectors are put into SVM classifier to recognize different fault type. The results of experiment of bearing fault diagnosis demonstrate that the method based on LCD information entropy feature and SVM is able to identify the bearing faults accurately and effectively,and the diagnosis effect is better than the method based on empirical mode decomposition（ EMD） information entropy and SVM. |
| format | Article |
| id | doaj-art-028cbf21477c4aaca723222216c76a89 |
| institution | Kabale University |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2015-01-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-028cbf21477c4aaca723222216c76a892025-01-10T14:03:06ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392015-01-013914414829921203Mechanical Fault Diagnosis based on LCD Information Entropy Feature and SVMZhang QiantuFang LiqingZhao YulongLv YanA new approach for mechanical fault diagnosis based on local characteristic- scale decomposition（ LCD） information entropy feature and support vector machine（ SVM） is proposed. Firstly,the fault mechanical vibration signal is decomposed by using the LCD to obtain a certain number of intrinsic scale component（ ISC）. Secondly,combined with information entropy theory,the singular spectrum entropy in time domain,power spectrum entropy in frequency domain,feature space entropy,marginal spectrum entropy and momentary energy entropy in time- frequency domain are defined and used as the feature vector. At last,the feature vectors are put into SVM classifier to recognize different fault type. The results of experiment of bearing fault diagnosis demonstrate that the method based on LCD information entropy feature and SVM is able to identify the bearing faults accurately and effectively,and the diagnosis effect is better than the method based on empirical mode decomposition（ EMD） information entropy and SVM.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2015.12.031Local characteristic-scale decomposition（LCD）Information entropySupport vector machine（SVM）Feature extractionFault diagnosis |
| spellingShingle | Zhang Qiantu Fang Liqing Zhao Yulong Lv Yan Mechanical Fault Diagnosis based on LCD Information Entropy Feature and SVM Jixie chuandong Local characteristic-scale decomposition（LCD） Information entropy Support vector machine（SVM） Feature extraction Fault diagnosis |
| title | Mechanical Fault Diagnosis based on LCD Information Entropy Feature and SVM |
| title_full | Mechanical Fault Diagnosis based on LCD Information Entropy Feature and SVM |
| title_fullStr | Mechanical Fault Diagnosis based on LCD Information Entropy Feature and SVM |
| title_full_unstemmed | Mechanical Fault Diagnosis based on LCD Information Entropy Feature and SVM |
| title_short | Mechanical Fault Diagnosis based on LCD Information Entropy Feature and SVM |
| title_sort | mechanical fault diagnosis based on lcd information entropy feature and svm |
| topic | Local characteristic-scale decomposition（LCD） Information entropy Support vector machine（SVM） Feature extraction Fault diagnosis |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2015.12.031 |
| work_keys_str_mv | AT zhangqiantu mechanicalfaultdiagnosisbasedonlcdinformationentropyfeatureandsvm AT fangliqing mechanicalfaultdiagnosisbasedonlcdinformationentropyfeatureandsvm AT zhaoyulong mechanicalfaultdiagnosisbasedonlcdinformationentropyfeatureandsvm AT lvyan mechanicalfaultdiagnosisbasedonlcdinformationentropyfeatureandsvm |