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Real Time Cardan Shaft State Estimation of High-Speed Train Based on Ensemble Empirical Mode Decomposition

机译:基于集成经验模态分解的高速列车万向轴实时状态估计

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Due to the special location and structure of transmission system on high-speed train named CRH5, dynamic unbalance state of the cardan shaft will pose a threat to the train servicing safety, so effective methods that test the cardan shaft operating information and estimate the performance state in real time are needed. In this study a useful estimation method based on ensemble empirical mode decomposition (EEMD) is presented. By using this method, time-frequency characteristic of cardan shaft can be extracted effectively by separating the gearbox vibration acceleration data. Preliminary analysis suggests that the pinions rotating vibration separated from gearbox vibration by EEMD can be used as important assessment basis to estimate cardan shaft state. With two sets gearbox vibration signals collected from the in-service train at different running speed, the comparative analysis verifies that the proposed method has high effectiveness for cardan-shaft state estimate. Of course, it needs further research to quantify the performance state of cardan shaft based on this method.
机译:由于高速列车CRH5传动系统的特殊位置和结构,万向轴的动态不平衡状态将对列车的维修安全构成威胁,因此,有效的方法可以测试万向轴的运行信息并评估性能状态。需要实时。在这项研究中,提出了一种有用的基于整体经验模式分解(EEMD)的估计方法。通过这种方法,通过分离齿轮箱的振动加速度数据可以有效地提取万向轴的时频特性。初步分析表明,小齿轮旋转振动与变速箱振动的分离可以作为估算万向轴状态的重要评估依据。通过对在役列车在不同行驶速度下采集到的两组齿轮箱振动信号的比较分析,验证了该方法对万向轴状态估计的有效性。当然,基于这种方法对万向轴的性能状态进行量化还需要进一步的研究。

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