针对列车运行过程中的振动信号是复杂非线性的，并且单一通道的信号存在着信息不完全的问题，提出了一种车体和转向架上多个通道信号融合的抗蛇行减振器故障诊断的方法。首先，对列车多个通道的信号进行自适应噪声完备集合经验模态分解（complete ensemble empirical mode decomposition with adaptive noise, CEEMDAN），提取分解后的本征模态函数（intrinsic mode function, IMF）精细复合多尺度散布熵（refined composite multiscale dispersion entropy, RCMDE）组成特征集；其次，用核主成分分析法（kernel principal component analysis, KPCA）对提取出的特征集进行降维；最后，将最优特征子集输入到蛇优化的核极限学习机（snake optimized kernel extreme learning machine, SO-KELM）中来诊断抗蛇行减振器故障类型。试验结果表明，经过核主成分分析法优选过后的多通道融合特征集能够准确反映抗蛇行减振器不同故障类型信号特征，实现了抗蛇行减振器的故障诊断，并将蛇优化核极限学习机与其他模型对比验证了该方法的优越性。

Aiming at the problem that the vibration signals in train operation are complex and nonlinear, and the information of single channel signal is incomplete, a fault diagnosis method of yaw damper based on multi-channel signal fusion on car body and bogie was proposed. Firstly, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was performed on the signals of multiple train channels, and the intrinsic mode function (IMF) was extracted to compose the feature set of refined composite multiscale dispersion entropy. Secondly, kernel principal component analysis (KPCA) was used to reduce the dimensionality of the extracted feature set. Finally, the optimal feature subset was inputted into the snake optimized kernel extreme learning machine(SO-KELM) to diagnose the yaw damper fault types. The experimental results show that the multi-channel fusion feature set optimized by kernel principal component analysis can accurately reflect the signal characteristics of different fault types of yaw damper, and realize the fault diagnosis of yaw damper. The superiority of this method is verified by comparing with other models.