基于Kalman-GARCH模型的结构损伤识别

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摘要

基于监测数据的结构损伤识别，是桥梁健康监测系统发挥感知预警效益的重要基础。为进一步提高结构损伤识别精度，提出一种融合Kalman滤波与广义自回归条件异方差(GARCH)模型的结构损伤识别方法。采用Kalman滤波对加速度时程数据进行降噪处理，在此基础上，建立了线性递归AR模型，对结构损伤进行识别；引入非线性递归GARCH模型，进一步提高识别精度；利用加速锈蚀损伤钢筋混凝土梁动力试验获取的加速度时程数据，对算法的有效性进行验证。结果表明：以损伤前后时间序列模型残差方差比为特征指标，能够有效识别结构损伤；与Kalman-AR模型相比，Kalman-GARCH模型能够解释部分非线性特征，弥补AR模型忽略数据异方差性所带来的识别误差，识别精度提高了14.2%。该方法可为基于海量数据的桥梁结构状态感知提供一种新的思路。

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	周建庭1
	2
	李晓庆1
	辛景舟1
	2
	阳珊清1
	周应新3
	4

关键词 ：桥梁结构, 损伤识别, Kalman滤波, 时间序列, 广义自回归条件异方差(GARCH)

Abstract：

The structural damage identification based on monitoring data plays a fundamental role in the bridge health monitoring system to exert the benefit of perception and early warning.To further improve the accuracy of structural damage identification, a new method was proposed which integrates the Kalman filtering and generalized autoregressive conditional heteroskedasticity (GARCH) model.First, the Kalman filter was used to de-noise the raw acceleration data, and a linear recursive autoregressive model (AR) was established to identify the structural damage.Then, the nonlinear recursive GARCH model was introduced to further improve the identification accuracy.Finally, the time-history data obtained in the tests of corroded RC beams was used to verify the effectiveness of the proposal algorithm.The results show that the residual variance ratio of the time series model can be effectively used to identify the structural damage.Compared to Kalman-AR model, the Kalman-GARCH model can explain the nonlinear characteristics and make up the recognition error caused by neglecting the heteroskedasticity of data.The accuracy can be improved by 14.2%.The results provide a new way for bridge structure state perception based on massive data.

Key words： bridge structure damage identification Kalman filtering time series generalized autoregressive conditional heteroskedasticity(GARCH)

收稿日期: 2018-10-24 出版日期: 2020-03-15

引用本文:

周建庭1,2，李晓庆1，辛景舟1,2，阳珊清1，周应新3,4. 基于Kalman-GARCH模型的结构损伤识别[J]. 振动与冲击, 2020, 39(6): 1-7.
ZHOU Jianting1,2，LI Xiaoqing1，XIN Jingzhou1,2，YANG Shanqing1，ZHOU Yingxin3,4. Structural damage identification based on a Kalman-GARCH model. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(6): 1-7.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2020/V39/I6/1

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