基于Vold-卡尔曼滤波器的轴箱加速度阶次跟踪
Vold-Kalman Filter Order tracking of Axle Box Accelerations for Railway Stiffness Assessment
摘要：
面对日益增长的成本和效率需求，智能数据驱动的监控程序在确保铁路基础设施的安全运营和优化管理方面具有巨大的潜力。大量研究表明，轨道刚度是影响退化演变的主要参数之一，从而驱动维护过程。因此，轨道刚度的测量是表征轨道在劣化率和噪声排放方面的性能的基础。这可以通过低成本的车载监控（OBM）传感系统（即，轴箱加速度计），这些加速度计安装在运行中的列车上，能够对铁路基础设施网络进行频繁的实时监测。基于加速度的刚度指标在监测应用中很少被考虑。在这项工作中，使用一个Vold-卡尔曼滤波器提出，用于分解成周期性的车轮和轨道相关的激励-响应对功能的信号。我们证明，这些组件反过来与操作条件，如车轮的圆度和轨道类型。我们进一步说明了轨道刚度之间的关系，测量的轮轨力和轨枕通过幅度，这最终可以作为一个指标，预测轨道维修和预测的轨道耐久性。
英文摘要：
Intelligent data-driven monitoring procedures hold enormous potential for ensuring safe operation and optimal management of the railway infrastructure in the face of increasing demands on cost and efficiency. Numerous studies have shown that the track stiffness is one of the main parameters influencing the evolution of degradation that drives maintenance processes. As such, the measurement of track stiffness is fundamental for characterizing the performance of the track in terms of deterioration rate and noise emission. This can be achieved via low-cost On Board Monitoring (OBM) sensing systems (i.e., axle-box accelerometers) that are mounted on in-service trains and enable frequent, real-time monitoring of the railway infrastructure network. Acceleration-based stiffness indicators have seldom been considered in monitoring applications. In this work, the use of a Vold-Kalman filter is proposed, for decomposing the signal into periodic wheel and track related excitation--response pairs functions. We demonstrate that these components are in turn correlated to operational conditions, such as wheel out-of-roundness and rail type. We further illustrate the relationship between the track stiffness, the measured wheel-rail forces and the sleeper passage amplitude, which can ultimately serve as an indicator for predictive track maintenance and prediction of track durability.
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