>> Nature Journal >> 2023 >> Issue 1 >> 正文
Incipient weak fault diagnosis for rolling bearing based on ARLMD and IMOMEDA
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 EnergyPower and Mechanical EngineeringNorth China Electric Power UniversityBaoding  071003HebeiChina2.Changchun Power Supply CompanyState Grid Jilin Electric Power Company Co.Ltd.Changchun  130021JilinChina

Abstract:In order to realize the weak fault identification of rolling bearing under the working condition of background noise interferencea new diagnosis method combining adaptive robust local mean decompositionARLMDand improved multipoint optimal minimum entropy deconvolution adjustedIMOMEDAwas proposed.Firstlyin order to effectively improve the SNR of original signalARLMD algorithm based on Spearman correlation coefficient was used to process the original signaland 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 lengthan improved MOMEDA method was proposed based on residual autocorrelation energy ratioRAER.IMOMEDA was executed on the optimal component to enhance and amplify the periodic impact features.Finallythe characteristic frequency information was extracted from the envelope spectrum of deconvolution signal.The simulationexperiment and engineering signal analysis results showed that the proposed method could effectively extract the weak fault features under strong noise environmentand could realize the accurate diagnosis of bearing damage.

Key words:rolling bearing;incipient faults;robust local mean decomposition;deconvolution;residual autocorrelation energy ratio

 基于ARLMD和IMOMEDA的滚动轴承早期微弱_唐贵基.pdf

Lastest