Research on improved modal parameter identification method using Hilbert-Huang transform
In the wind tunnel test, the full-bridge aeroelastic model needs to simulate both the shape and dynamic characteristics of the real bridge, and modal parameters are key dynamic parameters. Therefore, it is essential to identify the modal parameters of the model accurately. To get accurate modal para...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2023-11-01
|
| Series: | Measurement + Control |
| Online Access: | https://doi.org/10.1177/00202940231173752 |
| _version_ | 1797631111423066112 |
|---|---|
| author | Mingjin Zhang Hongyu Chen Tingyuan Yan Hao Sun Lianhuo Wu |
| author_facet | Mingjin Zhang Hongyu Chen Tingyuan Yan Hao Sun Lianhuo Wu |
| author_sort | Mingjin Zhang |
| collection | DOAJ |
| description | In the wind tunnel test, the full-bridge aeroelastic model needs to simulate both the shape and dynamic characteristics of the real bridge, and modal parameters are key dynamic parameters. Therefore, it is essential to identify the modal parameters of the model accurately. To get accurate modal parameters of the long-span bridge model in the wind tunnel test, the applications of the Hilbert-Huang transform for modal parameter identification were analyzed in this paper. Then a band-pass filter is designed to filter the original signal so that the intrinsic mode function obtained by empirical mode decomposition can satisfy the single-component signal requirement and eliminate the mode mixing effect effectively. Meanwhile, the endpoint data extension method based on SVM (Support Vector Machine) was presented to restrain the end effects of empirical mode decomposition. Finally, taking the Oujiang Bridge as the engineering background, the improved algorithm was applied to modal parameter identification of the bridge under ambient excitation. The modal parameters such as modal frequency and damping ratio were obtained. The reliability of the improved method was verified by comparing the identified modal parameters with the results of the finite element method, and it turns out that the improved method can reduce the frequency identification error of vertical bend, lateral bend, and torsion to 1.01%, 4.07%, and 1.68%. The results indicated that the improved method based on the Hilbert-Huang transform can accurately identify the main modal parameters of the structure and can be better applied to identify the modal parameters of long-span bridge structures. |
| first_indexed | 2024-03-11T11:17:40Z |
| format | Article |
| id | doaj.art-9edace14ebb0463a99778795fb931fe3 |
| institution | Directory Open Access Journal |
| issn | 0020-2940 |
| language | English |
| last_indexed | 2024-03-11T11:17:40Z |
| publishDate | 2023-11-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Measurement + Control |
| spelling | doaj.art-9edace14ebb0463a99778795fb931fe32023-11-10T19:35:05ZengSAGE PublishingMeasurement + Control0020-29402023-11-015610.1177/00202940231173752Research on improved modal parameter identification method using Hilbert-Huang transformMingjin Zhang0Hongyu Chen1Tingyuan Yan2Hao Sun3Lianhuo Wu4Department of Bridge Engineering, Southwest Jiaotong University, Chengdu, Sichuan, ChinaDepartment of Bridge Engineering, Southwest Jiaotong University, Chengdu, Sichuan, ChinaDepartment of Bridge Engineering, Southwest Jiaotong University, Chengdu, Sichuan, ChinaCCCC Second Highway Consultants Co., Ltd., Wuhan, Hubei, ChinaDepartment of Bridge Engineering, Southwest Jiaotong University, Chengdu, Sichuan, ChinaIn the wind tunnel test, the full-bridge aeroelastic model needs to simulate both the shape and dynamic characteristics of the real bridge, and modal parameters are key dynamic parameters. Therefore, it is essential to identify the modal parameters of the model accurately. To get accurate modal parameters of the long-span bridge model in the wind tunnel test, the applications of the Hilbert-Huang transform for modal parameter identification were analyzed in this paper. Then a band-pass filter is designed to filter the original signal so that the intrinsic mode function obtained by empirical mode decomposition can satisfy the single-component signal requirement and eliminate the mode mixing effect effectively. Meanwhile, the endpoint data extension method based on SVM (Support Vector Machine) was presented to restrain the end effects of empirical mode decomposition. Finally, taking the Oujiang Bridge as the engineering background, the improved algorithm was applied to modal parameter identification of the bridge under ambient excitation. The modal parameters such as modal frequency and damping ratio were obtained. The reliability of the improved method was verified by comparing the identified modal parameters with the results of the finite element method, and it turns out that the improved method can reduce the frequency identification error of vertical bend, lateral bend, and torsion to 1.01%, 4.07%, and 1.68%. The results indicated that the improved method based on the Hilbert-Huang transform can accurately identify the main modal parameters of the structure and can be better applied to identify the modal parameters of long-span bridge structures.https://doi.org/10.1177/00202940231173752 |
| spellingShingle | Mingjin Zhang Hongyu Chen Tingyuan Yan Hao Sun Lianhuo Wu Research on improved modal parameter identification method using Hilbert-Huang transform Measurement + Control |
| title | Research on improved modal parameter identification method using Hilbert-Huang transform |
| title_full | Research on improved modal parameter identification method using Hilbert-Huang transform |
| title_fullStr | Research on improved modal parameter identification method using Hilbert-Huang transform |
| title_full_unstemmed | Research on improved modal parameter identification method using Hilbert-Huang transform |
| title_short | Research on improved modal parameter identification method using Hilbert-Huang transform |
| title_sort | research on improved modal parameter identification method using hilbert huang transform |
| url | https://doi.org/10.1177/00202940231173752 |
| work_keys_str_mv | AT mingjinzhang researchonimprovedmodalparameteridentificationmethodusinghilberthuangtransform AT hongyuchen researchonimprovedmodalparameteridentificationmethodusinghilberthuangtransform AT tingyuanyan researchonimprovedmodalparameteridentificationmethodusinghilberthuangtransform AT haosun researchonimprovedmodalparameteridentificationmethodusinghilberthuangtransform AT lianhuowu researchonimprovedmodalparameteridentificationmethodusinghilberthuangtransform |