Vehicle Parameter Identification through Particle Filter using Bridge Responses and Estimated Profile
Vehicle Parameter Identification through Particle Filter using Bridge Responses and Estimated Profile
复制标题
使用桥响应和估计轮廓通过粒子滤波器识别车辆参数
DOI：
10.1016/j.proeng.2017.04.458
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Su Di
中科院分区：
文献类型：
--
作者：
Wang Haoqi;Nagayama Tomonori;Su Di
由于车辆荷载可能引起疲劳和大振动等问题，因此需要对在桥梁上行驶的车辆的重量进行评估。通过测量桥梁的应变响应来评估车辆载荷的桥梁动态称重系统已经被提出。然而，在桥梁构件处安装应变计通常是昂贵且耗时的，限制了实际应用。通过车桥相互作用系统的数值模拟，研究了利用不同传感器位置的桥梁加速度响应数据识别车重的方法。识别的参数不仅限于车辆重量，悬架刚度和阻尼系数也被识别。一个数据同化技术称为粒子滤波的贝叶斯理论的基础上识别的参数。使用相同的粒子滤波技术，从配备传感器的探测车的动态响应的时间历史的轮廓估计。所提出的方法具有抗噪声的质量参数识别。
The weight of vehicles driving over bridges needs to be evaluated because the vehicle load potentially causes problems such as fatigue and large vibration. Bridge Weigh-In-Motion systems to evaluate vehicle load by measuring strain response of bridges have been proposed. However, the installation of strain gauges at bridge members are often costly and time consuming, limiting practical applications. This paper investigates the identification of vehicle weight using bridge acceleration response data at different sensor locations through the numerical simulation of vehicle-bridge interaction system. The identified parameters are not limited to the vehicle weight; suspension stiffness and damping coefficients are also identified. A data assimilation technique known as particle filter based on the Bayesian theory is employed to identify the parameters. The time history of the profile is estimated using the same particle filter technique from the dynamic response of a probe vehicle equipped with sensors. The proposed method is shown to have robustness against noises for the mass parameter identification.