STRUCTURAL DAMAGE IDENTIFICATION BASED ON LMD SAMPLE ENTROPY AND RBF NETWORK
Adaptive time frequency analysis based on local mean decomposition and nonlinear quantization ability of sample entropy,combined with radial basis function（ RBF） neural network. A method of structural damage identification based on local mean decomposition（ LMD） sample entropy and radial basis funct...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2018-01-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2018.03.003 |
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| author | WANG MingYue MIAO BingRong LI Xu Juan YANG ZhongKun |
| author_facet | WANG MingYue MIAO BingRong LI Xu Juan YANG ZhongKun |
| author_sort | WANG MingYue |
| collection | DOAJ |
| description | Adaptive time frequency analysis based on local mean decomposition and nonlinear quantization ability of sample entropy,combined with radial basis function（ RBF） neural network. A method of structural damage identification based on local mean decomposition（ LMD） sample entropy and radial basis function neural network is proposed. Firstly,the original signal is decomposed into a number of product function components（ PF component） by LMD to the original signal of structure vibration.Then extract the sample entropy of the first 3 PF components to realize the feature quantization of the PF component. Finally,the sample entropy of the component is used as the damage characteristic vector. The radial basis function neural network is used to identify the bottom plate of scaled carbody for high-speed train. The experimental results show that while this method is used to identify structural damage,the damage identification errors of location and degree are 96. 97% and 96. 25% respectively. The validity and accuracy of this method in structural damage diagnosis are proved. |
| format | Article |
| id | doaj-art-7660ccd3c02b476ba3b18841dad32d1c |
| institution | DOAJ |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2018-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-7660ccd3c02b476ba3b18841dad32d1c2025-08-20T02:41:55ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692018-01-014052252730601878STRUCTURAL DAMAGE IDENTIFICATION BASED ON LMD SAMPLE ENTROPY AND RBF NETWORKWANG MingYueMIAO BingRongLI Xu JuanYANG ZhongKunAdaptive time frequency analysis based on local mean decomposition and nonlinear quantization ability of sample entropy,combined with radial basis function（ RBF） neural network. A method of structural damage identification based on local mean decomposition（ LMD） sample entropy and radial basis function neural network is proposed. Firstly,the original signal is decomposed into a number of product function components（ PF component） by LMD to the original signal of structure vibration.Then extract the sample entropy of the first 3 PF components to realize the feature quantization of the PF component. Finally,the sample entropy of the component is used as the damage characteristic vector. The radial basis function neural network is used to identify the bottom plate of scaled carbody for high-speed train. The experimental results show that while this method is used to identify structural damage,the damage identification errors of location and degree are 96. 97% and 96. 25% respectively. The validity and accuracy of this method in structural damage diagnosis are proved.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2018.03.003Damage identificationLocal mean decompositionSample entropyRadial basis function neural network |
| spellingShingle | WANG MingYue MIAO BingRong LI Xu Juan YANG ZhongKun STRUCTURAL DAMAGE IDENTIFICATION BASED ON LMD SAMPLE ENTROPY AND RBF NETWORK Jixie qiangdu Damage identification Local mean decomposition Sample entropy Radial basis function neural network |
| title | STRUCTURAL DAMAGE IDENTIFICATION BASED ON LMD SAMPLE ENTROPY AND RBF NETWORK |
| title_full | STRUCTURAL DAMAGE IDENTIFICATION BASED ON LMD SAMPLE ENTROPY AND RBF NETWORK |
| title_fullStr | STRUCTURAL DAMAGE IDENTIFICATION BASED ON LMD SAMPLE ENTROPY AND RBF NETWORK |
| title_full_unstemmed | STRUCTURAL DAMAGE IDENTIFICATION BASED ON LMD SAMPLE ENTROPY AND RBF NETWORK |
| title_short | STRUCTURAL DAMAGE IDENTIFICATION BASED ON LMD SAMPLE ENTROPY AND RBF NETWORK |
| title_sort | structural damage identification based on lmd sample entropy and rbf network |
| topic | Damage identification Local mean decomposition Sample entropy Radial basis function neural network |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2018.03.003 |
| work_keys_str_mv | AT wangmingyue structuraldamageidentificationbasedonlmdsampleentropyandrbfnetwork AT miaobingrong structuraldamageidentificationbasedonlmdsampleentropyandrbfnetwork AT lixujuan structuraldamageidentificationbasedonlmdsampleentropyandrbfnetwork AT yangzhongkun structuraldamageidentificationbasedonlmdsampleentropyandrbfnetwork |