基于小波包能量法的简支梁损伤仿真识别研究

doi:10.16182/j.issn1004731x.joss.17-0186

系统仿真学报 ›› 2019, Vol. 31 ›› Issue (6): 1201-1207.doi: 10.16182/j.issn1004731x.joss.17-0186

基于小波包能量法的简支梁损伤仿真识别研究

刘习军, 陶宪坤, 张素侠

天津大学机械工程学院,天津 300350

收稿日期:2017-05-02 修回日期:2018-08-14 出版日期:2019-06-08 发布日期:2019-12-12
作者简介:刘习军(1956-),男,河北,本科,教授,研究方向为非线性振动理论及应用、液固耦合动力学、桥梁动力学;陶宪坤(1989-),男,山东,硕士生,研究方向为桥梁损伤识别。
基金资助:
国家自然科学基金(51479136)

Simulation Research on Damage Identification of Simply Supported Beam Based on Wavelet Packet Energy Method

Liu Xijun, Tao Xiankun, Zhang Suxia

School of Mechanical Engineering,Tianjin University, Tianjin 300350, China

Received:2017-05-02 Revised:2018-08-14 Online:2019-06-08 Published:2019-12-12

摘要/Abstract

摘要： 通过ANSYS软件建立简支梁模型,用MATLAB软件对其位移响应进行小波包分析。采用小波包能量曲率差作为指标进行损伤识别,研究了损伤数量、损伤位置、损伤程度、测点疏密和移动荷载速度等参数对简支梁桥损伤结果的影响,提出分步识别方法,解决稀疏测点情况下的损伤识别问题,并将小波包能量曲率差归一化,研究该方法对噪声的鲁棒性。结果表明小波包能量法能有效、准确地识别梁的损伤。

关键词: 简支梁, 小波包能量, 损伤模拟, 损伤识别

Abstract: A finite element model of simply-supported beam is built by ANSYS;the displacement response of the beam is used to make wavelet packet analysis with MATLAB to get the curvature difference of WP-E (wavelet packet energy);and the studies on the effects of different parameters on damage recognition results of simple beam bridge are carried out, such as damage locations, damage level, measuring point density and speed of moving loads. For the condition of less measuring points, a method of arranging the measuring points by two steps is proposed. The normalized curvature difference of WP-E is made to study the robustness of this method to noise. The results show that the wavelet packet-energy method is effective and accurate in the damage detection of simply supported beam.

Key words: simply supported beam, wavelet packet-energy, damage simulation, damage identification

中图分类号:

TP391.9

刘习军, 陶宪坤, 张素侠. 基于小波包能量法的简支梁损伤仿真识别研究[J]. 系统仿真学报, 2019, 31(6): 1201-1207.

Liu Xijun, Tao Xiankun, Zhang Suxia. Simulation Research on Damage Identification of Simply Supported Beam Based on Wavelet Packet Energy Method[J]. Journal of System Simulation, 2019, 31(6): 1201-1207.

参考文献

[1] 葛继平, 李胡生. 基于小波包能量累积变异的梁桥损伤识别试验[J]. 振动、测试与诊断, 2011, 31(1): 85-88.
Ge Jiping, Li Husheng.Experiment on Damage Detection of Girder Bridge Using Wavelet Packet Energy Accumulated Variation[J]. Journal of Vibration Measurement & Diagnosis, 2011, 31(1): 85-88.
[2] 刘习军, 商开然, 张素侠, 等. 基于小波包变换的梁式结构损伤定位方法[J]. 实验力学, 2015, 30(3): 305-312.
Liu Xijun, Shang Kairan, Zhang Suxia, et al.On the Damage Localization of Beam Structure Based on Wavelet Packet Transform[J]. Journal of Experimental Mechanics, 2015, 30(3): 305-312.
[3] 刘习军, 商开然, 张素侠, 等. 基于改进小波包能量的梁式结构损伤识别[J]. 振动与冲击, 2016, 35(13): 179-185.
Liu Xijun, Shang Kairan, Zhang Suxia, et al.Damage Detection Method for Beam Structures Based on Improved Wavelet Packet Energy[J]. Journal of Vibration and Shock, 2016, 35(13): 179-185.
[4] 余竹, 夏禾, Goicolea J M, 等. 基于小波包能量曲率差法的桥梁损伤识别试验研究[J]. 振动与冲击, 2013, 32(5): 20-25.
Yu Zhu, Xia He, Goicolea JM, et al.Experimental study on bridge damage identification based on wavelet packet energy curvature difference method[J]. Journal of Vibration and Shock, 2013, 32(5): 20-25.
[5] Jiangpeng Shu, Ziye Zhang, Ignacio Gonzalez, et al.The Application of a Damage Detection Method Using Artificial Neural Network and Train-induced Vibrations on a Simplified Railway Bridge Model[J]. Engineering Structures (S0141-0296), 2013, 52: 408-421.
[6] Jun Zhao, John N Ivan, John T De Wolf. Structure Damage Detection Using Artificial Neural Networks[J]. Journal of Infrastructure Systems (S1943-555X), 1998, 4: 93-101.
[7] 管德清, 荣政, 汤博文. 基于小波神经网络的框架结构损伤识别[J]. 长沙理工大学学报(自然科学版), 2014, 11(1): 37-41.
Guan Deqing, Rong Zheng, Tang Bowen.Damage Identification of Frame Structure Wavelet and Neural Network Analysis[J]. Journal of Changsha University of Science and Technology (Natural Science), 2014, 11(1): 37-41.
[8] 王承, 陈光, 谢永乐. 小波-神经网络在模拟电路故障诊断中的应用[J]. 系统仿真学报, 2005, 17(8): 1936-1938.
Wang Cheng, Chen Guang, Xie Yongle.Applying Wavelet Transform and Neural Networks to Fault Diagnosis in Analog Circuits[J]. Journal of System Simulation, 2005, 17(8): 1936-1938.
[9] 彭永涛, 王跃萍, 王霄婷. 基于神经网络性能模型的无人机起飞决断技术研究[J]. 系统仿真学报, 2015, 27(11): 2797-2803.
Peng Yongtao, Wang Yueping, Wang Xiaoting.UAV Take off Decision Based on Neural Network Model of Take off Capability[J]. Journal of System Simulation, 2015, 27(11): 2797-2803.
[10] 杨建国. 小波分析及其工程应用[M]. 北京: 机械工业出版社, 2005.
Yang Jianguo.Wavelet Analysis and its Engineering Applications[M]. Beijing: China Machine Press, 2005.
[11] 艾伦·V·奥本海姆, 艾伦·S·威尔斯基, S·哈米德·纳瓦布. 信号与系统[M]. 刘树棠译. 西安: 西安交通大学出版社, 2010.
Alan V Oppenheim, Alan S Willsky, S Hamid Nawab. Signals & Systems[M]. Xi’an: Xi’an Jiao-tong University Press, 2010.
[12] 童峥嵘, 郭尊礼, 曹晔, 等. 小波包变换对CO-OFDM系统高峰均比的抑制性能研究[J]. 光子学报, 2015, 44(12): 1-6.
Tong Zhengrong, Guo Zunli, Cao Ye, et al.Wavelet Packet Transform for PAPR Reduction Performance in CO-OFDM System[J]. Acta Photonica Sinica, 2015, 44(12): 1-6.
[13] 刘习军, 相林杰, 张素侠. 基于小波分析的简支梁桥损伤识别[J]. 振动、测试与诊断, 2015, 35(5): 866-872.
Liu Xijun, Xiang Linjie, Zhang Suxia, Damage Identification of Simply Supported Beam Bridges Based on Wavelet Analysis[J]. Journal of Vibration Measurement & Diagnosis, 2015, 35(5): 866-872.
[14] Chin-Teng Lin.Single-Channel Speech Enhancement in Variable Noise-Level Environment[J]. IEEE Transactions on Systems, Man, and Cybernetics—PART A: Systems and Humans (S1083-4427), 2003, 33: 137-144.

基于小波包能量法的简支梁损伤仿真识别研究

Simulation Research on Damage Identification of Simply Supported Beam Based on Wavelet Packet Energy Method

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