Structural damage identification based on ARMAV model and J-divergence

LI Meng1,2, GUO Huiyong1,2

Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (1) : 123-130.

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PDF(3334 KB)
Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (1) : 123-130.

Structural damage identification based on ARMAV model and J-divergence

  • LI Meng1,2, GUO Huiyong1,2
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Abstract

Damage detection technique plays a fundamental role in the Structural Health Monitoring (SHM) system. To further improve the accuracy and applicability of damage identification, a new method is proposed, which integrates J-divergence distance function and Vector Autoregressive Moving Average (ARMAV) model. First, pre-whitening filter was used to cancel the correlation of excitations and de-noise the acceleration time series. Then, a ARMAV model was established, whose autoregressive parameters and residual variance were used to develop a damage indicator for damage identification. Finally, to verify the effectiveness of this method, standard data sets of laboratory three-story frame was used, and the damage identification experimental study on the relay tower model was carried out. The results show that the damage identification method based on ARMAV model and J- divergence distance is easy to operate, and can accurately and efficiently localize the damage of the frame and relay tower structure. Furthermore, the proposed method is less affected by environmental changes, and is promising to be used in online structural health monitoring systems.

Key words

damage identification / experimental study / ARMAV model / J-divergence / time series analysis

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LI Meng1,2, GUO Huiyong1,2. Structural damage identification based on ARMAV model and J-divergence[J]. Journal of Vibration and Shock, 2024, 43(1): 123-130

References

[1] CHUAN Z D, CATBAS F N. A review of computer vision–based structural health monitoring at local and global levels [J]. Structural Health Monitoring, 2021, 20(2): 692–743. [2] CARDEN E P, FANNING P. Vibration based condition monitoring: a review [J]. Structural Health Monitoring, 2004, 3(4): 355-377. [3] HOU R, XIA Y. Review on the new development of vibration-based damage identification for civil engineering structures: 2010–2019 [J]. Journal of Sound and Vibration, 2020, 491. [4] BURGOS D A T, VARGAS R C G, PEDRAZA C, AGIS D, POZO F. Damage identification in structural health monitoring: a brief review from its implementation to the use of data-driven applications [J]. Sensors, 2020, 20(3). [5] YANG Y, DORN C, MANCINI T, TALKEN Z, THEILER J, KENYON G, FARRAR C, MASCAREÑAS D. Reference-free detection of minute, non-visible, damage using full-field, high-resolution mode shapes output-only identified from digital videos of structures [J]. Structural Health Monitoring, 2018, 17(3): 514-531. [6] 王真, 程远胜. 基于时间序列模型自回归系数灵敏度分析的结构损伤识别方法[J]. 工程力学, 2008, 25(10): 38−43, 49. WANG Zhen, CHENG Yuansheng. Structural damage identification based on sensitivity analysis of autoregressive coefficients of time series models [J]. Engineering Mechanics, 2008, 25(10): 38−43, 49. [7] JAYAWARDHANA M, ZHU X, LIYANAPATHIRANA R, GUNAWARDANA U. Statistical damage sensitive feature for structural damage detection using ar model coefficients [J]. Advances in Structural Engineering, 2015, 18(10): 1551-1562. [8] 郭惠勇, 王志华, 李正良. 基于自回归条件异方差转换指标的非线性损伤识别[J]. 西南交通大学学报, 2020, 55(3): 459−466, 517, 456. GUO Huiyong, WANG Zhihua, LI Zhengliang. Structural nonlinear damage identification based on autoregressive conditional heteroskedasticity conversion index [J]. Journal of Southwest Jiaotong University, 2020, 55(3): 459 − 466, 517, 456. [9] ZHU H, YU H, GAO F, WENG S, SUN Y, HU Q. Damage identification using time series analysis and sparse regularization [J]. Structural Control and Health Monitoring, 2020, 27(9). [10] NAIR K K, KIREMIDJIAN A S, LAW K H. Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure [J]. Journal of Sound and Vibration, 2006, 291(1): 349-368. [11] NAIR K K, KIREMIDJIAN A S. Time series based structural damage detection algorithm using gaussian mixtures modeling [J]. Journal of Dynamic Systems, Measurement, and Control, 2007,129(3): 285-293. [12] ZHENG H, MITA A. Damage indicator defined as the distance between ARMA models for structural health monitoring [J]. Structural Control and Health Monitoring, 2008, 15(7): 992-1005. [13] ZHENG H, MITA A. Localized damage detection of structures subject to multiple ambient excitations using two distance measures for autoregressive models [J]. Structural Health Monitoring-An International Journal, 2009, 8(6): 573-573. [14] 刘纲, 罗钧, 方鹏, 秦阳. 基于向量自回归模型的损伤识别方法[J]. 振动. 测试与诊断, 2015, 35(5): 873-879, 990. LIU Gang, LUO Jun, FANG Peng, QIN Yang. Damage identification method based on vector autoregressive model [J]. Journal of Vibration,Measurement & Diagnosis, 2015, 35(5): 873-879, 990. [15] 张玉建, 罗永峰, 郭小农, 刘俊, 朱钊辰. 基于时间序列模型的结构损伤识别方法[J]. 同济大学学报(自然科学版), 2019, 47(12): 1691-1700, 1755. ZHANG Yujian, LUO Yongfeng, GUO Xiaonong, LIU Jun, ZHU Zhaochen. Structural damage identification method based on time series model [J]. Journal of Tongji University Natural Science, 2019, 47(12): 1691-1700, 1755. [16] LARDIES J. Modal parameter identification based on ARMAV and state-space approaches [J]. Archive of Applied Mechanics, 2010, 80(4): 335-352. [17] ZHANG C, MOUSAVI A A, MASRI S F, GHOLIPOUR G, YAN K, LI X. Vibration feature extraction using signal processing techniques for structural health monitoring: a review [J]. Mechanical Systems and Signal Processing, 2022,177. [18] KULLBACK S. Information Theory and Statistics (Dover Books on Mathematics) [M]. Wiley, 1959. [19] 王桂增, 董东, 方崇智. 基于Kullback信息测度的长输管线的泄漏检测[J]. 信息与控制, 1989(01): 14-18. WANG Guizeng, DONG Dong, FANG Chongzhi. Leakage detection of long-distance pipeline based on Kullback information measure [J]. Information and Control, 1989(01): 14-18. [20] BOGERT B P., HEALY M , TUKEY J W. The quefrency alanysis of time series for echoes: cepstrum, pseudo-autocovariance, cross-cepstrum, and saphe cracking [J]. Proceedings of the Symposium on Time, 1963, pp. 209–243. [21] FIGUEIREDO E, PARK G, FIGUEIRAS J, FARRAR C, WORDEN K. Structural Health Monitoring Algorithm Comparisons Using Standard Data Sets, Report of Los Alamos National Laboratory, 2009, p. 115.
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