摘要
采用SSA-VMD算法对滚动轴承振动信号进行去噪处理,并用主成分分析法(PCA)对故障信号做进一步降维处理,最后用支持向量机(SVM)训练分类模型。用测试集进行故障诊断实验,结果表明:SSA-VMD算法能够有效诊断滚动轴承的故障类型,准确率高达96.25%。
In this paper,the SSA-VMD algorithm was used to denoise bearing’s vibration signals,including having the principal component analysis(PCA)employed to further reduce the dimension of the fault signals and the support vector machine(SVM)adopted to train classification model,as well as the test set used for fault diagnosis experiment.Experimental results show that,this method can effectively diagnose various faults in rolling bearings and the accuracy rate can be 96.25%.
作者
刘均
赵喜民
程增喜
LIU Jun;ZHAO Xi-min;CHENG Zeng-xi(School of Electrical Engineering&Information,Northeast Petroleum University;China Resources Power(Xuzhou)Co.,Ltd.)
出处
《化工自动化及仪表》
CAS
2021年第6期630-633,共4页
Control and Instruments in Chemical Industry
