一种IMNGO-VMD小样本数据下的轴承故障识别方法

A bearing fault identification method based on IMNGO-VMD small sample data

针对滚动轴承发生故障时信息提取不充分、可用故障样本少等问题,文章提出一种改进的北方苍鹰优化算法(IMNGO)来优化变分模态分解(VMD)和支持向量机(SVM)进行小样本的轴承故障识别。首先使用云平台采集实验数据,然后利用IMNGO算法对VMD进行参数优化找到最佳的本征模态分量(IMF),构建特征向量能量谱和主元贡献图筛选最佳的IMF分量。然后将提取的特征信息导入到IMNGO优化后的SVM中进行轴承的小样本检测识别。经过IMNGO优化后,单工况下的识别准确率达到了99.20%,复杂工况下的识别准确率达到了94.45%。小样本数据下,文章提出的方法相对于传统的检测方法识别准确率有了大幅提升。

In this paper,an improved northern goshawk optimization algorithm(IMNGO)is proposed to optimize the variational mode decomposition(VMD)and support vector machine(SVM)for small-sample bearing fault identification.This algorithm can effectively solve the problems of insufficient information extraction and few available fault samples when the rolling bearing fails.The experimental data is collected through the cloud platform,and the VMD parameters are optimized using the IMNGO algorithm to find the best intrinsic mode component(IMF),construct the eigenvector energy spectrum and the principal component contribution map,and screen the best IMF component.Finally,the extracted feature information is imported into the SVM optimized by IMNGO for small sample detection and recognition of bearings.After IMNGO optimization,the recognition accuracy rate under single working conditions reached 99.20%,and the recognition accuracy rate under complex working conditions reached 94.45%.Under the small sample data,the method proposed in this paper has greatly improved the recognition accuracy compared with the traditional detection method.