Time: 2023-01-10 | Counts: |
TANG G J, DING A, WANG X L, et al.Incipient weak fault diagnosis for rolling bearing based on ARLMD and IMOMEDA[J].Journal of Henan Polytechnic University(Natural Science) ,2023,42(1):102-115.
doi:10.16186/j.cnki.1673-9787.2021060085
Received:2021/06/22
Revised:2021/10/20
Published:2023/01/25
Incipient weak fault diagnosis for rolling bearing based on ARLMD and IMOMEDA
TANG Guiji1, DING Ao1,2, WANG Xiaolong1, ZHANG Ye2, JIANG Chao2, LI Haiming2
1.School of Energy,Power and Mechanical Engineering,North China Electric Power University,Baoding 071003,Hebei,China;2.Changchun Power Supply Company,State Grid Jilin Electric Power Company Co.,Ltd.,Changchun 130021,Jilin,China
Abstract:In order to realize the weak fault identification of rolling bearing under the working condition of background noise interference,a new diagnosis method combining adaptive robust local mean decomposition(ARLMD)and improved multipoint optimal minimum entropy deconvolution adjusted(IMOMEDA)was proposed.Firstly,in order to effectively improve the SNR of original signal,ARLMD algorithm based on Spearman correlation coefficient was used to process the original signal,and then the best component containing rich fault information was screened out by L-kurtosis maximum principle.To solve the limitation which the accuracy of MOMEDA was affected by filter length,an improved MOMEDA method was proposed based on residual autocorrelation energy ratio(RAER).IMOMEDA was executed on the optimal component to enhance and amplify the periodic impact features.Finally,the characteristic frequency information was extracted from the envelope spectrum of deconvolution signal.The simulation,experiment and engineering signal analysis results showed that the proposed method could effectively extract the weak fault features under strong noise environment,and could realize the accurate diagnosis of bearing damage.
Key words:rolling bearing;incipient faults;robust local mean decomposition;deconvolution;residual autocorrelation energy ratio