基于ARMAV模型和J-散度的结构损伤识别

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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。

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关键词 ：损伤识别, 实验研究, ARMAV模型, J-散度, 时间序列分析

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

收稿日期: 2022-11-17 出版日期: 2024-01-15

引用本文:

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. JOURNAL OF VIBRATION AND SHOCK, 2024, 43(1): 123-130.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2024/V43/I1/123

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