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.

中文翻译：

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通过卡尔曼滤波技术估计横向轨道不平整度

这项工作的目的是开发一种基于模型的方法，用于基于使用安装在在役列车上的惯性传感器来监测横向轨道不规则性。为此，使用陀螺仪测量轮对偏航角速度，使用两个加速度计测量轮对和转向架框架的横向加速度。使用能够捕获最相关动态行为的高度简化的线性转向架模型允许设置非常有效的基于卡尔曼的监控策略。通过使用在具有真实不规则性的直线轨道上运行的在役车辆的详细多体模型来评估设计滤波器的行为。模型输出用于生成虚拟测量，随后用于运行过滤器和验证建议的估计器。此外，基于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，进行了系统的参数分析。使用所提出的方法获得的结果是有希望的，显示出在直线轨道上监测横向对准的高精度和鲁棒性，并且计算成本非常低。