To improve the accuracy of Operational Path Analysis with Exogeneous Inputs (OPAX) model by excluding the noise interference sufficiently in the vehicle operating condition data (time-domain vibration signal), the combined noise reduction method of Ensemble Empirical Mode Decomposition (EEMD) and wavelet threshold was used. Since the noise content of each noisy intrinsic mode functions (IMFs) decomposed by EEMD is uncertain, the effective signal element in the less noisy IMFs affects the accuracy of the first-layer wavelet coefficients to estimate the noise variance, the EEMD and wavelet particle swarm optimization sample entropy threshold denoising (EEMD-WPSE) method is presented in terms of information entropy. In this method, the sample entropy of the eliminated noise is used as the information cost function, together with the particle swarm optimization algorithm to find the optimal wavelet threshold of each high-frequency noisy IMFs. After denoising the simulation signal, it is found that the combination of EEMD-WPSE threshold with hard threshold function, soft threshold function and half-soft threshold function identifying higher SNR and lower RMSE, are given to demonstrate the higher universality of the proposed method. The method is applied to the noise reduction processing of the automobile operating condition data for constructing the OPAX model, and the degree of similarity between the synthesized responses of the care-target point obtained by the OPAX model and the measured responses under the second order operational condition are observed, as it turned out, the calculation results of SNR and RMSE indicated that EEMD-WPSE can better promote the accuracy of OPAX model in terms of noise reduction.

为了通过充分排除车辆运行状况数据（时域振动信号）中的噪声干扰，提高外源输入（OPAX）模型的运行路径分析的准确性，采用了组合经验模态分解（EEMD）和小波相结合的降噪方法使用了阈值。由于由EEMD分解的每个噪声本征模函数（IMF）的噪声含量是不确定的，因此噪声较小的IMF中的有效信号元素会影响第一层小波系数的准确性，以估计噪声方差，EEMD和小波粒子群从信息熵的角度提出了优化样本熵阈值去噪（EEMD-WPSE）方法。在这种方法中，将消除后的噪声的样本熵用作信息成本函数，结合粒子群优化算法，找到每个高频噪声IMF的最优小波阈值。对模拟信号进行去噪后，发现EEMD-WPSE阈值与硬阈值函数，软阈值函数和半软阈值函数相结合，可以识别出较高的SNR和较低的RMSE，从而证明了该方法的较高通用性。该方法适用于汽车运行条件数据的降噪处理，以构建OPAX模型，并通过OPAX模型获得的护理目标点的综合响应与二阶操作下测得的响应之间的相似度。事实证明，情况已经观察到，