Damage identification method of cable-stayed bridges based onCEEMD and statistical parameters

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Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (19) : 326-336.

LIU Jie1,2,3, DING Xue1,3, LIU Qingkuan1,2,3, WANG Hailong4, BU Jianqing1,5

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Abstract

In order to solve the problem that the signal decomposition of cable-stayed bridge using only CEEMD method contains noisy IMF components and cannot quantify damage, a damage identification method of cable-stayed bridge based on CEEMD and statistical parameters is proposed. Through this method based CEEMD method, the amplitude standard deviation of the auxiliary white noise sequence is determined and integration times of CEEMD method is derived, Then the IMF components of each order are obtained; Euclidean distance is used for the pedigree cluster analysis of decomposed IMF components to avoid modal aliasing; the effective weight kurtosis index method with kurtosis statistical parameters is used to filter out noisy IMF components, extract active IMF components and reconstruct them into the sum of active IMF components; the indices of damage location identification are deduced by using statistical parameters of coefficient of variation, second-order central difference method and Taylor expansion, and the quantitative damage indices were deduced by using the statistical parameters of fourth-order statistical moment kurtosis. The damage identification of a cable-stayed bridge is studied by using the proposed method. The results show that the accuracy of damage location identification is 100%, and that the maximum error of damage quantification is 1.87%; under the interference of white Gaussian noise, the damage location identification is not affected, and the maximum error of damage quantification is 1.89%; the damage identification of the real bridge is carried out, and the results show that the real bridge girder is free of damage.

Key words

Cable-stayed bridge / Damage identification method / Complementary ensemble empirical mode decomposition (CEEMD) / Statistical parameters / Damage quantification / Noise interference

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LIU Jie1, 2, 3, DING Xue1, 3, LIU Qingkuan1, 2, 3, WANG Hailong4, BU Jianqing1, 5. Damage identification method of cable-stayed bridges based onCEEMD and statistical parameters[J]. Journal of Vibration and Shock, 2024, 43(19): 326-336

References

[1] 张喜刚，田雨，陈艾荣. 多灾害作用下桥梁设计方法研究综述[J]. 中国公路学报, 2018, 31(09): 7-19.
Xigang Zhang, Yu Tian, Airong Chen. Review of Bridge Design Method for Multiple Hazards[J]. China Journal of Highway and Transport, 2018, 31(09): 7-19.
[2] Vagnoli M, Remenyte-Prescott R, Andrews J. Railway bridge structural health monitoring and fault detection: State-of-the-art methods and future challenges[J]. Structural Health Monitoring, 2018, 17(4): 971-1007.
[3] 王凌波，王秋玲，朱钊，等. 桥梁健康监测技术研究现状及展望[J]. 中国公路学报, 2021, 34(12): 25-45.
Lingbo Wang, Qiuling Wang, Zhao Zhu, et al. Current Status and Prospects of Research on Bridge Health Monitoring Technology[J]. China Journal of Highway and Transport, 2021, 34(12): 25-45.
[4] 张笑华，肖兴勇，方圣恩. 面向桥梁结构健康监测的压缩感知动力响应信号重构[J]. 振动工程学报, 2022, 35(03): 699-706.
Xiaohua Zhang, Xingyong Xiao, Sheng'en Fang. Dynamic response reconstruction for bridge structural health monitoring based on compressed sensing[J]. Journal of Vibration Engineering, 2022, 35(03): 699-706.
[5] 刘玉驰，蒋玉峰，王树青，等. 基于数据融合及残差卷积自编码器的结构损伤识别方法[J]. 振动与冲击, 2023, 42(04): 194-203.
Yuchi Liu, Yufeng Jiang, Shuqing Wang, et al. Data fusion and residual convolutional auto-encoder based structural damage identification[J]. Journal of Vibration and Shock, 2023, 42(04): 194-203.
[6] 蒋雍建，刘逸平，周立成，等. 基于桥梁响应统计信息的智能损伤识别方法[J]. 合肥工业大学学报(自然科学版), 2019, 42(12): 1667-1671.
Yongjian Jiang, Yiping Liu, Licheng Zhou, et al. Intelligent damage identification based on statistical parameters of bridge responses[J]. Journal of Hefei University of Technology (Natural Science), 2019, 42(12): 1667-1671.
[7] 罗烨钶，陈永高，李升才. 基于经验小波变换-噪声辅助分析的桥梁信号降噪方法[J]. 振动与冲击, 2022, 41(21): 246-256.
Yeke Luo, Yonggao Chen, Shengcai Li. Adaptive denoising method of bridge vibration signal based on EWT-oise aided analysis theory[J]. Journal of Vibration and Shock, 2022, 41(21): 246-256.
[8] 邱阳，李盛，金亮，等. 基于统计特征混合与随机森林重要性排序的桥梁异常监测数据识别方法[J]. 传感技术学报, 2022, 35(06): 756-762.
Yang Qiu, Sheng Li, Liang Jin, et al. Bridge Anomaly Monitoring Data Identification Method Based on the Statistical Feature Mixture and Random Forest Permutation Importance Index[J]. Chinese Journal of Sensors and Actuators, 2022, 35(06): 756-762.
[9] 张涛，丁碧云，赵鑫. 采用改进的希尔伯特黄变换的损伤检测特征提取方法[J]. 西安交通大学学报, 2018, 52(10): 16-23.
Tao Zhang, Biyun Ding, Xin Zhao. A Feature Extraction Method of Defect Detection Using Improved Hilbert-Huang Transform[J]. Journal of Xi 'an Jiaotong University, 2018, 52(10): 16-23.
[10] 李雪艳，刘力菖. 利用振动响应协方差参数和数据融合的损伤识别方法[J]. 振动工程学报, 2021, 34(01): 141-149.
Xueyan Li, Lichang Liu. Adoption of vibration response covariance and data fusion for damage identification[J]. Journal of Vibration Engineering, 2021, 34(01): 141-149.
[11] YEH J, SHIEH J, HUANG N E. COMPLEMENTARY ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOVEL NOISE ENHANCED DATA ANALYSIS METHOD[J]. Advances in Adaptive Data Analysis, 2010, 02(02): 135-156.
[12] 邢世玲，吕双双，朱利明，等. 基于ICEEMDAN的连续梁桥车致振动信号的HHT分析[J]. 西南交通大学学报, 2021, 56(03): 477-484.
Shiling Xing, Shuangshuang Lu, Liming Zhu, et al. Hilbert-Huang Transfer Analysis on Vehicle-Induced Vibration Signal of Continuous Bridges Based on ICEEMDAN[J]. 2021, 56(03): 477-484.
[13] 季群策，何晓晖，张梅军，等. 基于EEMD和归一化IMF能量差的结构损伤识别[J]. 机械强度, 2018, 40(03): 545-551.
Qunce Ji, Xiaohui He, Meijun Zhang, et al. Structural damage identification based on EEMD and Normalized Energy Difference of IMF[J]. Journal of Mechanical Strength, 2018, 40(03): 545-551.
[14] 姜绍飞，陈志刚，沈清华，等. 基于EEMD与FastICA的损伤异常识别与定位[J]. 振动与冲击, 2016, 35(01): 203-209.
Shaofei Jiang, Zhigang Chen, Qinghua Shen, et al. Structural damage identification based on EEMD and Normalized Energy Difference of IMF[J] Journal of Mechanical Strength. 2016, 35(01): 203-209.
[15] 张爽，王晓东，李祥，等. 基于FVMD的滚动轴承故障特征提取方法[J]. 振动与冲击, 2022, 41(06): 236-244.
Shuang Zhang, Xiaodong Wang, Xiang Li, et al. Extraction method of rolling bearing fault characteristics based on FVMD[J]. Journal of Vibration and Shock, 2022, 41(06): 236-244.
[16] Gu R, Chen J, Hong R, et al. Incipient fault diagnosis of rolling bearings based on adaptive variational mode decomposition and Teager energy operator[J]. Measurement, 2020, 149: 106941.
[17] FAN Q, IKEJO K, NAGAMURA K, et al. Application of statistical parameters and discrete wavelet transform to gear damage diagnosis[J]. Journal of Advanced Mechanical Design, Systems, and Manufacturing, 2014, 8(2): 1-12.
[18] 冯增朝，沈永星. 基于表面位移的岩石失稳前兆识别研究[J]. 岩石力学与工程学报, 2021, 40(S2): 3024-3032.
Zengchao Feng, Yongxing Shen. Study on precursor identification of rock instability based on surface displacement[J]. Chinese Journal of Rock Mechanics and Engineering, 2021, 40(S2): 3024-3032.
[19] 刘光耀，刘习军，张素侠，等. 基于小波分析和变异系数的简支梁桥损伤识别[J]. 应用力学学报, 2020, 37(05): 1915-1922.
Guangyao Liu, Xijun Liu, Suxia Zhang, et al. Damage identification of simply supported beam bridge based on wavelet analysis and variation coefficient[J]. Chinese Journal of Applied Mechanics, 2020, 37(05): 1915-1922.
[20] 陈永高，钟振宇. 基于改进EEMD算法的桥梁结构响应信号模态分解研究[J]. 振动与冲击, 2019, 38(10): 23-30.
Yonggao Chen, Zhenyu Zhong. Modal decomposition of response signals for a bridge structure based on the improved EEMD[J]. Journal of Vibration and Shock, 2019, 38(10): 23-30.
[21] Yang K, Wang G, Dong Y, et al. Early chatter identification based on an optimized variational mode decomposition[J]. Mechanical Systems and Signal Processing, 2019, 115: 238-254.
[22] Ma J, Wu J, Wang X. Incipient fault feature extraction of rolling bearings based on the MVMD and Teager energy operator[J]. ISA Transactions, 2018, 80: 297-311.
[23] 王海龙，刘杰，王新敏，等. 建立斜拉桥基准有限元模型的新方法与实现[J]. 振动.测试与诊断, 2014, 34(03): 458-462.
Hailong Wang, Jie Liu, Xinmin Wang, et al. A new method and implementation of building baseline FE model of cable-stayed bridge[J]. Journal of Vibration, Measurement &. Diagnosis, 2014, 34(03): 458-462.