江阴大桥船撞期间实测数据时间同步分析

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振动与冲击 ›› 2018, Vol. 37 ›› Issue (14) : 10-21.

论文

江阴大桥船撞期间实测数据时间同步分析

王伟东 1，姜绍飞 1，周华飞 2，韩悦 2，杨蜜 2

作者信息 +

Time synchronization for monitoring data of the Jiangyin Bridge subjected to a ship collision

WANG Weidong¹, JIANG Shaofei¹, ZHOU Huafei², HAN Yue², YANG Mi²

Author information +

文章历史 +

摘要

为了解决江阴大桥船撞期间实测数据的时间不同步问题，提出了采用状态空间模型方法识别不同位置处加速度间的时间延迟。首先，任意选择其中一个加速度作为参考信号，而其余加速度作为时间平移信号，相对参考信号进行时间平移。然后，以一个参考信号和一个时间平移信号作为输出变量，针对每个平移时刻，分别建立相应的状态空间模型。模型参数由随机子空间识别方法计算得到，模型阶次由AIC (Akaike’s information criterion)和FPE (Final prediction error)估计得到。由于模型拟合数据的时间不同步将导致所建模型的损失函数增大，因此，可将损失函数达到最小值时所对应的平移时间作为加速度数据的实际时间延迟。此外，利用江阴大桥船撞前两小时的异步加速度数据及船撞后的同步加速度数据，分别对状态空间模型方法的可重复识别能力及抗伪识别能力进行了检验。结果表明，无论是异步数据还是同步数据，状态空间模型均能较准确地识别时间延迟。

Abstract

To address the asynchronicity issue of the multiple sensor data of the Jiangyin Bridge measured during a shipbridge collision, a statespace (SS) model was proposed to identify the time lag between the asynchronous accelerations at different locations of the bridge. First, one of the accelerations was randomly chosen as a reference signal, and the time axes of the rest of them (termed as time shifted signals) were individually shifted relatives to that of the reference signal with a series of time instant. Then, the SS model with two output variables, i.e., one reference signal and one time shifted signal, was formulated in correspondence with each time instant. The system matrices were computed by a datadriven stochastic subspace identification algorithm and the model order was estimated by the Akaike’s information theoretic criterion (AIC) and final prediction error (FPE). If the two accelerations for model fitting are asynchronous, errors may be introduced into the formulated SS model and its loss function is expected to be greater than the counterpart obtained with synchronous accelerations. Therefore, the actual time lag between them can be identified from the time instant that corresponds to the minimum of loss function. To evaluate the reproducibility of the SS model for time synchronization, asynchronous acceleration data measured two hours ahead of the shipbridge collision were analyzed as well. In addition, the synchronous acceleration data measured long after the shipbridge collision were utilized to examine its antifalseidentification capability. The results show that the SS model achieves a satisfactory performance in the identification of the time lag for both asynchronous and synchronous measurement data.

导出引用

王伟东 1，姜绍飞 1，周华飞 2，韩悦 2，杨蜜 2. 江阴大桥船撞期间实测数据时间同步分析[J]. 振动与冲击, 2018, 37(14): 10-21

WANG Weidong1, JIANG Shaofei1, ZHOU Huafei2, HAN Yue2, YANG Mi2. Time synchronization for monitoring data of the Jiangyin Bridge subjected to a ship collision[J]. Journal of Vibration and Shock, 2018, 37(14): 10-21

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