基于HHT能量和最大Lyapunov指数的蛇行分类方法

Classification Method of hunting based on HHT energy and

摘要:

现有蛇行监测标准仅考虑构架横向加速度信号的时域幅值信息,通过单一的固定值去评判列车是否蛇行,没有定量反映蛇行失稳对列车的影响程度。针对以上问题,提出基于HHT的能量法,该方法从信号频域主频的大小,频谱的集中性以及频率值等方面来判断高速列车是否发生蛇行运动。在此基础上提出快速蛇行收敛的概念,此类情况对车辆系统运行安全影响较小,因此将其认为是不影响车辆系统安全的信号,并对其进行识别。利用最大Lyapunov指数表征信号的周期性从而区分快速蛇行收敛和蛇行失稳(小幅蛇行和大幅蛇行)。结果表明,通过HHT能量法与最大Lyapunov指数相结合可以对高速列车正常运行、快速蛇行收敛、小幅蛇行和大幅蛇行的四种运行状态进行定性识别,且通过HHT能量值定量反映蛇行程度的大小。最后通过实测线路数据验证了方法的可行性。

Abstract:

The existing hunting monitoring standard only considers the time domain amplitude information of the lateral acceleration signal of the frame. A single fixed value is used to judge whether the train is hunting, which does not reflect the influence of hunting instability on the train quantitatively. To address the above issues, the HHT energy is firstly proposed to judge whether the high-speed train is hunting from the main frequency size, spectrum concentration and frequency value of the signal frequency domain. On this basis, this paper proposes the concept of fast hunting convergence, which has little influence on the safety of vehicle system operation, so this paper considers it as the signal that does not affect the safety of the vehicle system and identifies it. In this paper, the Largest Lyapunov exponent is used to characterize the periodicity of the signal so as to distinguish between fast hunting convergence and hunting instability (small amplitude hunting and large amplitude hunting). In conclusion, the four operating states of normal operation, fast hunting convergence, small amplitude hunting and large amplitude hunting of the vehicle system are identified qualitatively by combining the HHT energy with the Largest Lyapunov exponent. The HHT energy values reflects the intensity of hunting movement quantitatively. Finally, the feasibility of the method is verified by the measured data of the line.