基于EWT-SVD方法的高速列车滚动轴承故障诊断

针对高速列车齿轮箱滚动轴承早期故障特征提取困难的情况,提出了基于经验小波变换(Empirical Wavelet Transform,EWT)和奇异值分解(Singular value decomposition,SVD)的轴承故障诊断方法。首先对信号进行EWT变换得到各阶固有模态分量,然后计算各阶固有模态分量的峭度值并选取较大峭度值对应的分量。将选取的分量构造矩阵进行正交化奇异值分解,选择合适的阶数重构信号,最后对重构信号进行Hilbert包络解调分析。分别对仿真信号和滚动轴承发生外环故障进行分析,可以较为清晰地看到滚动轴承故障特征。研究结果表明,结合EWT、峭度系数和SVD的诊断方法可以准确、快速地提取轴承故障信息,从而可以对滚动轴承进行有效诊断。

Fault Diagnosis of High-speed Train Rolling Bearings Based on EWT-SVD Method

Aiming at the difficulty in extracting early fault features of high-speed train gearbox rolling bearings,bearings fault diagnosis method based on empirical wavelet transform(EWT)and singular value decomposition(SVD)was proposed.Firstly,EWT was used to decompose the vibration signal into intrinsic modal components.Then the kurtosis of the intrinsic modal components was calculated and some of the intrinsic modal components were selected by the rule of kurtosis.The hankel matrix,which was constructed with the intrinsic modal components,was orthogonally executed through SVD.At last,the Hilbert envelope demodulation was adopted with the new signal to detect the fault information.Through analyzing the simulation signal and the outer vibration signal of fault rolling bearing respectively,the characteristics frequency could be clearly extracted.The results indicated that the diagnosis method of EWT and kurtosis coefficient and SVD could accurately and quickly extract the bearings fault information,so the rolling bearings can be diagnosed effectively.