基于白噪声统计特性与EEMD的高速列车横向减振器故障诊断

Lateral damper fault diagnosis of high-speed train based on statistical characteristics of white noise and EEMD

针对高速列车横向减振器故障信号非线性非平稳的特点,提出了基于白噪声统计特性与聚合经验模态分解(EEMD)相结合的故障诊断算法。利用经验模态分解(EMD)对故障信号进行去噪,然后对去噪后的信号进行EEMD分解,最后对用相关系数求得的最能反映振动信号的本征模态函数(IMF)计算排列组合熵。在240 km/h速度下,对高速列车横向减振器七种工况进行诊断,识别率达到91.8%。实验结果表明,与基于小波熵特征分析的算法相比,该算法具有更高的识别率和更强的抗噪性能。

Considering the nonlinearity and nonstationarity of the lateral damper fault signal of high-speed train, this paper proposed a fault diagnosis method by combining ensemble empirical mode decomposition (EEMD) with the characteristics of white noise. Using empirical mode decomposition (EMD) to denoise the original fault signal, then decomposing the denoised signal with EEMD. It calculated the permutation entropy of the IMF which best fitted the original signal based on correlation analysis.The recognition rate reached 91.8% when applying this algorithm to diagnose seven different lateral damper faults ofthe high-speed train at the speed of 240 km/h. The experimental results show that the algorithm has higher recognition rate and stronger anti-noise performance comparing with wavelet entropy feature analysis method.