高速列车横向蛇行失稳的EEMD-CNN-LSTM预测方法

Lateral hunting instability prediction model of high-speed train based on EEMD-CNN-LSTM

高速列车运行时出现横向蛇行失稳严重威胁到列车运行安全。目前大多数方法主要为蛇行失稳的在线识别,而忽略从正常到蛇行失稳过程中的小幅蛇行阶段。为此,提出一种EEMD-CNN-LSTM方法来预测小幅的演变趋势,进而分析是否会发生蛇行失稳。并且基于相关系数与能量特征提出一种新的指标来挑选EEMD的最优模态。该方法首先通过集合经验模态分解(ensemble empirical mode decomposition,EEMD)对原始信号分解得到n阶固有模态(intrinsic mode functions,IMF),然后挑选m阶最优的固有模态分量,最后将最优模态分量输入到构建的CNN-LSTM神经网络模型,并输出结果。根据在线实测数据实验结果:提出的方法能够准确预测小幅蛇行的变化趋势,预测准确率达到100%,且计算速度优于EEMD-LTSA方法,证明该方法的有效性。

The safety of train operation is seriously threatened by the lateral hunting instability of high-speed trains.At present,most of the methods focus on the identification of hunting instability,but ignore the small amplitude hunting state from normal to hunting instability.Therefore,a method based on EEMD-CNN-LSTM is proposed to predict the trend of small amplitude hunting,and then to analyze whether the hunting instability will occur.An optimal IMF selection index of EEMD based on correlation coefficient and energy features is proposed.Firstly,the original signal is decomposed by EEMD to obtain the IMFs,then the optimal IMFs is selected.Finally,the m-order optimal IMFs are input into the CNN-LSTM neural network model.The experimental results show that the evolution state of small amplitude hunting can be accurately predicted by EEMD-CNN-LSTM,and the prediction accuracy of the proposed method is 100%,which shows effectiveness of the method.