论文标题

通过卡尔曼过滤技术估算横向轨道不规则性

Estimation of lateral track irregularity through Kalman filtering techniques

论文作者

Munoz, S., Ros, J., Escalona, J. L.

论文摘要

这项工作的目的是开发一种基于模型的方法,用于根据使用安装在服务列车上的惯性传感器来监测横向轨道不规则性。为此,陀螺仪用于测量轮子偏航角速度,并使用两个加速度计来测量轮子的横向加速度和旋转器框架的横向加速度。使用能够捕获最相关的动态行为的高度简化的线性转型模型,可以设置非常有效的基于卡尔曼的监视策略。设计过滤器的行为是通过使用在具有现实不规则的直线上运行的服务车辆的详细多体模型来评估的。该模型输出用于生成虚拟测量值,这些虚拟测量随后用于运行过滤器并验证提出的估计器。另外,根据这些模拟,确定了简化模型的等效参数。为了证明所提出的技术的鲁棒性,已经进行了系统的参数分析。用建议的方法获得的结果很有希望,显示出很高的精度和鲁棒性,可在直线轨道上监测横向对准,计算成本非常低。

The aim of this work is to develop a model-based methodology for monitoring lateral track irregularities based on the use of inertial sensors mounted on an in-service train. To this end, a gyroscope is used to measure the wheelset yaw angular velocity and two accelerometers are used to measure lateral acceleration of the wheelset and the bogie frame. Using a highly simplified linear bogie model that is able to capture the most relevant dynamic behaviour allows for the set-up of a very efficient Kalman-based monitoring strategy. The behaviour of the designed filter is assessed through the use of a detailed multibody model of an in-service vehicle running on a straight track with realistic irregularities. The model output is used to generate virtual measurements that are subsequently used to run the filter and validate the proposed estimator. In addition, the equivalent parameters of the simplified model are identified based on these simulations. In order to prove the robustness of the proposed technique, a systematic parametric analysis has been performed. The results obtained with the proposed method are promising, showing high accuracy and robustness for monitoring lateral alignment on straight tracks, with a very low computational cost.

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