基于VMD的自适应复合多尺度模糊熵及其在滚动轴承故障诊断中的应用

提出了一种基于自适应多尺度模糊熵、ILS（迭代拉普拉斯得分）特征选择和粒子群优化支持向量机(PSO-SVM)的滚动轴承故障诊断方法。该方法采用变分模态分解对振动信号进行分解和重构，并计算重构信号的复合多尺度模糊熵；同时采用迭代拉普拉斯得分选择敏感故障特征，并将特征选择结果输入到基于粒子群优化支持向量机的多故障分类器进行识别。将提出的方法应用于滚动轴承试验数据分析。结果表明:该方法对试验数据的故障识别率为100%。并将基于ILS特征选择方法与基于SFS（sequential forward selection）特征选择进行了对比，表明基于SFS特征选择的最高识别率为92.86%，而基于ILS特征选择的故障识别率达到100%。 

VMD based adaptive composite multiscale fuzzy entropy and its application to fault diagnosis of rolling bearing

A rolling bearing fault diagnosis approach was proposed based on the adaptive multiscale fuzzy entropy, ILS (iterative Laplacian score) and PSO-SVM (particle swarm algorithm optimization support vector machine). In the proposed method, the variational mode decomposition was used for the decomposition and reconstruction. Then composite multiscale entropy fuzzy of the reconstructed signals was calculated. Besides, the iterative Laplacian score algorithm was used for the sensitive fault feature selection and the selected features were input to the PSO-SVM based classifier for training and recognition. Finally, the proposed method was applied to experiment data of rolling bearing. Results showed that the identifying rate of proposed method was 100%. Also, the ILS based feature selection was compared with the SFS (sequential forward selection) method; and the result indicated that the highest identifying fault rate of SFS based method was 92.86% while the identifying fault rate of the ILS based fault diagnosis method reached to 100%.