Aiming at the problem that the vibration signals of rolling bearing are susceptible to noise interference,which affects the accuracy of fault diagnosis,a rolling bearing fault diagnosis method combining improved local mean decomposition(LMD)interval threshold denoising algorithm,multi-scale permutation entropy(MPE)and support vector machine(SVM)is proposed.The improved LMD interval threshold denoising algorithm is used for signal preprocessing.Considering that the denoised signal still has strong nonlinear characteristics,the MPE algorithm is used to construct the feature vector set,and the signals are input into SVM for fault identification.The analysis results of the measured bearing signals show that the fault identification accuracy of the proposed fault diagnosis method is 98.3%,which is better than other fault diagnosis methods.
关键词
局部均值分解/阈值降噪/滚动轴承/故障诊断
Key words
local mean decomposition/threshold denoising/rolling bearing/fault diagnosis
维普
万方数据