Fault Diagnosis of Variable Speed Rolling Bearings Based on BAACMD-NGO-TMSST
It is difficult to extract time-varying fault features of rolling bearings under variable speed conditions,and the time-reas-signed multisynchrosqueezing transform(TMSST)is susceptible to noise interference,and the determination of its related parameters is not adaptive.Aiming at the problems,a fault diagnosis method for rolling bearings with variable speed based on BAACMD-NGO-TMSST was proposed.The fault signal was divided into multiple components by BAACMD,and the Gini index and envelope entropy were used as comprehensive indexes to select the optimal components,so as to remove the noise interference.The northern goshawk optimization algo-rithm(NGO)was used to optimize the parameters of the TMSST.Finally,the optimal components were subjected to time-frequency analysis by using the optimized TMSST,and the maximum TF envelope spectra(TFES)was calculated to extract fault features.The fea-sibility and effectiveness of the proposed method were verified by simulated signals and Ottawa bearing dataset.Compared with other noise reduction methods,BAACMD is superior in noise reduction;compared with other time-frequency analysis methods,the proposed method has better feature extraction effect.
NETL
NSTL
万方数据
