针对样本熵算法在相空间重构过程中存在冗余运算的问题，通过构建符号变量矩阵的方法，对样本熵算法的相空间重构过程进行替换，建立改进的样本熵算法。白噪声和粉噪声仿真信号分析表明，改进的样本熵算法能有效提取信号的特征，并且具有较高计算效率。以往复压缩机轴承间隙故障为研究对象，应用改进的样本熵算法对其进行特征提取，并与样本熵进行对比，该方法特征提取结果与样本熵算法保持高度一致，算法的计算效率远高于样本熵算法。

Aiming at the problem of redundancy in the phase space reconstruction of sample entropy algorithm, the phase space reconstruction process of sample entropy algorithm was replaced by a symbolic variable matrix, and an improved sample entropy algorithm was established. The analysis of white noise and powder noise simulation signals shows that the improved sample entropy algorithm can extract signal features effectively and has high computational efficiency. In the past, bearing clearance faults of complex compressors were studied, and the improved sample entropy algorithm was applied to extract features and compared with sample entropy. The feature extraction results of the method are highly consistent with the sample entropy algorithm, and the computational efficiency of the algorithm is much higher than that of the sample entropy algorithm.