基于小波包信息熵和SO-SVM的滚动轴承故障诊断

Fault diagnosis of rolling bearing based on wavelet packet entropy and SO-SVM

针对滚动轴承振动信号的特征提取和故障诊断,提出了一种基于小波包信息熵和蛇优化算法(SO)优化支持向量机的滚动轴承故障诊断方法。使用小波包处理采集到的振动信号,构建小波包的能谱熵和系数熵,将构建的特征向量输入SO-SVM进行识别和分类;最终实现多故障模式识别,输出诊断结果。通过仿真实验表明,此方法对五组不同的样本诊断准确率达到99.17%~100%,且相比于果蝇算法优化支持向量机(FOA-SVM)和粒子群算法优化支持向量机(PSO-SVM)具有更高的故障识别分类效果。

Aiming at the problem of feature extraction and fault diagnosis of rolling bearing vibration signals,a fault diagnosis method of rolling bearing based on wavelet packet information entropy and support vector machine(SVM)optimized by snake optimization algorithm(SO)is proposed.The collected vibration signals are processed by using the wavelet packet,the energy spectrum entropy and the coefficient entropy of the wavelet packet are constructed,and the constructed feature vectors are input into the SO-SVM for identification and classification;Finally,the multi-fault pattern recognition is realized and the diagnosis results are output.The simulation results show that the diagnostic accuracy of this method for five different groups of samples reaches 99.17%~100%,and compared with FOA-SVM and PSO-SVM,it has a higher effect of fault recognition and classification.