A two-stage damage recognition method based on data fusion

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Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (17) : 132-144.

ANG Xiaojuan1, LAN Xiangyong1, ZHOU Hongyuan1,2, WANG Lihui1, ZHANG Jian3

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Abstract

Due to the advantages of not requiring ambient excitation measurements, exhibiting high sensitivity to structural damage, and demonstrating robustness against noise, the structural damage detection method based on the cross-correlation function has garnered increased attention. Nevertheless, its application to large and complex structures with limited measurements poses challenges due to the substantial number of parameters needed to be identified, which would seriously affect the accuracy and computational efficiency of the identification results. To address this issue, a two-stage damage detection method utilizing data fusion is proposed, which involves an initial stage for damage localization, followed by a subsequent stage for damage qualification. During the initial damage localization stage, multi-type measurement data are applied for response reconstruction, and then a damage indicator, based on the change in cross-correlation functions among multi-type data before and after structural damage, is established for structural damage localization with the D-S evidence theory. During the subsequent damage quantification stage, employing the particle swarm-gradient algorithm, the cross-correlation function among multi-type measurement data is utilized for damage qualification. Numerical simulation and experimental validation results demonstrate that the proposed two-stage damage detection method based on data fusion could effectively improve the accuracy and computational efficiency of damage detection results. Additionally, it exhibits outstanding robustness in the face of measurement noise.

Key words

Cross-correlation function / Data fusion / Damage detection / Response reconstruction / D-S evidence theory

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ANG Xiaojuan1, LAN Xiangyong1, ZHOU Hongyuan1, 2, WANG Lihui1, ZHANG Jian3. A two-stage damage recognition method based on data fusion[J]. Journal of Vibration and Shock, 2024, 43(17): 132-144

References

[1] WEI F, QIAO P Z. Vibration-based damage identification methods: a review and comparative study[J]. Structural Health Monitoring, 2010,10(1):83-111.
[2] 王盟, 翁顺, 余兴胜, 等. 基于时变模态振型小波变换的结构损伤识别方法[J]. 振动与冲击, 2021,40(16):10-19.
WANG Meng, WENG Shun, YU Xing-sheng, et al. Structural damage identification based on time-varying modal mode shape of wavelet transformation[J]. Journal of vibration and shock, 2021,40(16):10-19.
[3] 简正坤, 刘习军, 张素侠. 基于应变指标和D-S证据理论的简支梁桥损伤识别[J]. 实验力学, 2019,34(5):791-799.
JIAN Zheng-kun, LIU Xi-jun, ZHANG Su-xia. Damage identification in simply supported beam bridge based on strain index and D-S evidence theory[J]. Journal of Experimental Mechanics, 2019,34(5):791-799.
[4] 巫生平, 蒋国平. 基于位移平均曲率差的桥梁结构损伤识别方法研究[J]. 振动与冲击, 2023,42(10):240-250.
WU Sheng-ping, JIANG Guo-ping. Research on damage identification method of bridge structure based on displacement average curvature difference[J]. Journal of Vibration and Shock, 2023,42(10):240-250.
[5] 王立新, 李雪艳, 姜慧, 等. 基于加速度二次协方差矩阵参数变化比法的环境振动下结构损伤识别[J]. 振动与冲击, 2016,35(8):143-158.
WANG Li-xin, LI Xue-yan, JIANG Hui, et al. Damage identification under ambient vibration based on change ratios of covariance of covariance matrix components of structural acceleration responses[J]. Journal of Vibration and Shock, 2016,35(8):143-158.
[6] NI P H, XIA Y, LAW S S, et al. Structural damage detection using auto/cross-correlation functions under multiple unknown excitations[J]. International Journal of Structural Stability and Dynamics, 2014,14(5):1440006.
[7] LI W, HUANG Y. A method for damage detection of a jacket platform under random wave excitations using cross correlation analysis and PCA-based method[J]. Ocean Engineering, 2020,214:107734.
[8] 王慧, 王乐, 田润泽. 基于时域响应相关性分析及数据融合的结构损伤检测研究[J]. 工程力学, 2020,37(9):30-37.
WANG Hui, WANG Le, TIAN Run-ze. Structural damage detection using correlation functions of time domain vibration responses and data fusion[J]. Engineering Mechanics, 2020,37(9):30-37.
[9] 缪炳荣, 张盈, 黄仲, 等. 利用模态应变能变化率的结构损伤识别优化方法[J]. 振动工程学报, 2023,36(2):477-486.
MIAO Bing-rong, ZHANG Ying, HUANG Zhong, et al. Structural damage identification optimization method using change rate of modal strain energy[J]. Journal of Vibration Engineering, 2023,36(2):477-486.
[10] 仇树茂, 杨海峰, 吴子燕, 等. 基于应变模态和稀疏贝叶斯学习的网架结构损伤识别[J]. 计算力学学报, 2022,39(1):63-69.
QIU Shu-mao, YANG Hai-feng, WU Zi-yan,et al. Damage identification of truss structure based on strain mode and sparse Bayesian learning[J]. Chinese Journal of Computational Mechanics, 2022,39(1):63-69.
[11] ZHANG F L, GU D K, LI X, et al. Structural damage detection based on fundamental Bayesian two-stage model considering the modal parameters uncertainty[J]. Structural Health Monitoring, 2022,22(4):2305-2324.
[12] 邹云峰, 卢玄东, 阳劲松, 等. 基于应变模态响应重构的损伤识别方法[J]. 工程力学, 2022,39(9):225-233.
ZOU Yun-feng, LU Xuan-dong, YANG Jin-song, et al. A damage identification method based on strain modal response reconstruction[J]. Engineering Mechanics, 2022,39(9):225-233.
[13] 张笑华, 任伟新, 方圣恩. 两种传感器的位置优化及结构多种响应重构[J]. 振动与冲击, 2014,33(18):26-30.
ZHANG Xiao-hua, REN Wei-xin, FANG Sheng-en. Location optimization of dual-type sensors for multi-kind structural response reconstruction[J]. Journal of Vibration and Shock, 2014,33(18):26-30.
[14] 肖永锋. 面向结构动力响应重构的多类型传感器优化布置研究[D]. 深圳: 哈尔滨工业大学; 2019.
XIAO Yong-feng. Research on optimal layout of multi-type sensors for structural dynamic response reconstruction[D]. Shenzhen: Harbin Institute of Technology;2019.
[15] 周宏元, 张广才, 王小娟, 等. 基于互相关函数的聚类蝴蝶优化算法的结构损伤识别[J]. 振动与冲击, 2021,40(17):189-196.
ZHOU Hong-yuan, ZHANG Guang-cai, WANG Xiao-juan, et al. Structural damage identification based on clustering butterfly optimization algorithm of cross-correlation function[J]. Journal of Vibration and Shock, 2021,40(17):189-196.
[16] WANG X J, CHENG F, ZHOU H Y, et al. Structural damage detection based on cross-correlation function with data fusion of various dynamic measurements[J]. Journal of Sound and Vibration, 2022,541:117373.
[17] Xiang C S , Liu H L , Zhou Y ,et al. Structural Damage Identification Method of Girder Bridges Based on Multilevel Data Fusion Theory[J]. Advances in Materials Science and Engineering, 2022,2022:1-21.
[18] Barman S K, Mishra M, Maiti D K, et al. Vibration-based damage detection of structures employing Bayesian data fusion coupled with TLBO optimization algorithm[J]. Structural and Multidisciplinary Optimization, 2021,64(4):2243-2266.
[19] 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(1):141-149.
LI Xue-yan, LIU Li-chang. Adoption of vibration response covariance and data fusion for damage identification[J]. Journal of Vibration Engineering, 2021,34(1):141-149.
[20] 胡鑫, 杨智春, 王乐. 基于振动响应内积向量和数据融合的结构损伤检测方法试验研究[J]. 振动与冲击, 2013,32(14):109-115.
Hu Xin, Yang Zhi-chun, Wang Le. Damage detection of structures by a combined approach of IPV method and data fusion[J]. Journal of Vibration and Shock, 2013,32(14):109-115.
[21] 万志敏. 基于贝叶斯理论的结构响应重构方法研究[D]. 武汉: 华中科技大学; 2015.
Wan Zhi-min. Structural dynamic response reconstruction based on the Bayesian theory[D]. Wuhan: Huazhong University of Science and Technology;2015.
[22] YAM L H, LEUNG T P, LI D B, et al. Theoretical and experimental study of modal strain analysis[J]. Journal of Sound and Vibration, 1996,191(2):251-260.
[23] SHI Z Y, LAW S S, ZHANG L M. Structural damage detection from modal strain energy change[J]. Journal of Engineering Mechanics-ASCE, 2000,126(12):1216-1223.
[24] ZHOU H Y, ZHANG G C, WANG X J, et al. A hybrid identification method on butterfly optimization and differential evolution algorithm[J]. Smart Structures and Systems, 2020,26(3):345-360.
[25] GUO N, YANG Z C, WANG L, et al. Dynamic model updating based on strain mode shape and natural frequency using hybrid pattern search technique[J]. Journal of Sound and Vibration, 2018,422:112-130.
[26] SUN H, BETTI R. Simultaneous identification of structural parameters and dynamic input with incomplete output-only measurements[J]. Structural Control Health Monitoring, 2014,21(6):868-889.
[27] CHEN P H, SHAHANDASHTI S M. Hybrid of genetic algorithm and simulated annealing for multiple project scheduling with multiple resource constraints[J]. Automation in Construction, 2009,18(4):434-443.
[28] WANG X J, ZHANG G C, WANG X M, et al. Output-only structural parameter identification with evolutionary algorithms and correlation functions[J]. Smart Materials and Structures, 2020,29(3):03501