对三路信号进行分离，基于峭度和基于负熵的独立分量分析（ICA）-The three way signal separation, based on the kurtosis and independent component analysis (ICA) based on negative entropy

基于灰度梯度最大熵的分割方法，先通过聚类进行改进做初分割然后再进行细分割。-Segmentation of gray gradient based on maximum entropy, first through the clustering is improved. At first, then the fine segmentation segmentation.

模糊熵，　为解决从信号中提取故障特征难的问题, 介绍了一种新的信号故障特征提取方法—— 多尺度熵( multi-scale entro py, 简称MSE) , 并将样本熵和多尺度熵分别应用于转子故障信号复杂性的度量, 以提取故障特征。试验数据 分析表明, 与样本熵相比, 多尺度熵更能有效地实现转子故障类型的诊断。-A new rolling bearing fault diagnosis approach based onmultiscale permutation entropy (

提供 基于粒子群算法优化的变分模态分解算法，适应度函数选择的是模糊熵(This paper presents a variational mode decomposition algorithm based on particle swarm optimization, and the selection of fitness function is fuzzy entropy)