针对滚动轴承故障振动信号的调制特征和传统包络分析法的缺陷，提出一种基于经验模式分解(Empirical Mode Decomposition，简称EMD)和谱峭度(Spectrum Kurtosis，简称SK)的改进包络谱滚动轴承故障诊断方法。该方法首先对滚动轴承故障振动信号进行经验模式分解，将其分解为多个固有模式函数(Intrinsic Mode Function，简称IMF)之和，然后对各IMF分量傅里叶变换后取其绝对值，并计算其谱绝对值平方包络，在此基础上再计算不同频带IMF分量谱平方包络的峭度，最后利用谱峭度的滤波器作用，选取由轴承缺陷所引起的共振频率所在频带的IMF分量，自动构建最佳包络来进行故障诊断。将该方法应用到滚动轴承内圈缺陷的仿真故障数据和实际数据中，分析结果表明了该方法的有效性。

According to the modulation characteristics of roller bearing fault vibration signals，the limitation of the traditional envelope analysis，a roller bearing fault diagnosis method using improved envelope analysis based on empirical mode decomposition(EMD) and spectrum kurtosis (SK) was proposed in this paper. Firstly, roller bearing fault vibration signals were decomposed into a finite number of intrinsic mode functions(IMFs); secondly, using fft transformation to make each IMF into complex signal and calculatting these complex signal absolute value, then, spectrum kurtosis value was calculated of each IMF spectrum square envelope; finally, using the filter function of spectrum kurtosis, the best frequency band for demodulation was automatically choseen by the criterion of IMFs spectrum kurtosis. The proposed method has been applied into simulated signal and actual signal, the analysis results demonstrated the effectiveness of the proposed method.