基于VMD与IFWA-SVM的滚动轴承故障诊断研究

为有效提取非平稳性、复杂性的滚动轴承振动信号特征,提出一种基于变分模态分解、改进烟花算法(IFWA)优化支持向量机(SVM)的滚动轴承故障诊断方法。利用VMD对原始信号进行分解,计算得到各IMF的样本熵,将原始信号的时域特征与其结合组成特征矩阵。为提高故障诊断效率,采用IFWA优化SVM,建立IFWA-SVM模型。使用训练集特征矩阵训练诊断模型,实现滚动轴承的故障诊断。利用实测信号验证该方法,并与粒子群算法优化进行比较。结果表明:利用该方法进行诊断,正确率提高了3.33%、训练时间缩短了21.55 s,验证了该方法的可行性。

Research on Rolling Bearing Fault Diagnosis Based on VMD and IFWA-SVM

In order to extract the vibration signal characteristics of non-stationary and complex rolling bearings,a rolling bearing fault diagnosis method based on variable modal decomposition (VMD) and improved fireworks algorithm (IFWA) optimization support vector machine (SVM) was proposed.The original signal was decomposed by using VMD,the sample entropies of each IMF were calculated,and the time domain indexes (TDI) of the original signal were combined with them to form a feature matrix.In order to improve fault diagnosis efficiency,IFWA was used to optimize SVM,and the IFWA-SVM model was established.The feature matrix was trained and tested to achieve fault diagnosis of rolling bearings.The method was verified by using measured signals,and compared with the particle swarm optimization (PSO) algorithm.The results show that by using the method,the accuracy is increased by 3.33% and the training time is shortened by 21.55 s,which verifies the feasibility of the method.