基于ARMAV模型和J-散度的结构损伤识别

PDF(3334 KB)

振动与冲击 ›› 2024, Vol. 43 ›› Issue (1) : 123-130.

论文

李孟1,2 郭惠勇1,2

作者信息 +

Structural damage identification based on ARMAV model and J-divergence

LI Meng1,2, GUO Huiyong1,2

Author information +

文章历史 +

摘要

损伤识别技术是结构健康监测系统的关键组成部分，为了进一步提高损伤识别的准确性和适用性，提出一种融合信息距离函数J-散度与向量自回归滑动平均（ARMAV）模型的损伤识别方法。首先采用预白化过滤器对加速度时域数据进行消除激励相关性以及降噪处理；然后建立了ARMAV模型，并由模型的自回归参数和残差方差构建损伤判别指标；最后采用三层框架实验数据，并进行转播塔模型的损伤识别实验研究验证了该方法的有效性。结果表明：基于ARMAV模型和J-散度距离的损伤识别方法可操作性强，能够准确、高效地定位框架和塔架结构的损伤，且该方法受环境变化的影响较小，可为在线结构健康监测提供一种新思路。

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.

导出引用

李孟1,2 郭惠勇1,2. 基于ARMAV模型和J-散度的结构损伤识别[J]. 振动与冲击, 2024, 43(1): 123-130

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

参考文献

[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.