Founded in 1971 >
Chinese Sci-tech Core Periodicals >
British Science Abstracts (SA, INSPEC) Indexed Journals >
United States, Cambridge Scientific Abstract: Technology (CSA: T) Indexed Journals >
United States, Ulrich's Periodicals Directory(UPD)Indexed Journals >
United States, Cambridge Scientific Abstract: Natural Science (CSA: NS) Indexed Journals >
Poland ,Index of Copernicus(IC) Indexed Journals >
International Standard Serial Number:
ISSN 1001-4551
Sponsor:
Zhejiang University;
Zhejiang Machinery and Electrical Group
Edited by:
Editorial of Journal of Mechanical & Electrical Engineering
Chief Editor:
ZHAO Qun
Vice Chief Editor:
TANG ren-zhong,
LUO Xiang-yang
Tel:
86-571-87041360,87239525
Fax:
86-571-87239571
Add:
No.9 Gaoguannong,Daxue Road,Hangzhou,China
P.C:
310009
E-mail:
meem_contribute@163.com
Abstract: Aiming at the problem that the vibration signal of wind turbine bearings affected by strong background noise and other equipment excitation sources leaded to the difficulty of feature extraction for early bearing weak fault features, a wind turbine bearing fault diagnosis method based on maximum correlation kurtosis deconvolution (MCKD), optimized by multi-objective beluga whale optimization (MOBWO) algorithm was proposed. Firstly, based on the powerful global and local search capabilities of MOBWO, the key parameters of MCKD were optimized, and the optimal parameter combination of MOBWO was obtained. Secondly, the optimized MCKD was employed to process the original signal by deconvolution operation for eliminating the influence of background noise and other equipment excitation sources and highlighting the bearing periodic pulse signal. Then, the envelope spectrum method was used to process the deconvolution signal to perform the extraction of bearing fault characteristic frequency, and the obtained fault characteristic frequency values were compared with the theoretical calculation values for diagnosis. Finally, in order to validate the effectiveness of MOBWO-MCKD, the experiments were conducted on the actual collected inner and outer ring fault data of wind turbine bearings. The results show that the fault feature extraction method based on MOBWO-MCKD can effectively eliminate the background noise and other excitation source interference of the early bearing weak fault features. The inner ring signal envelope spectrum shows the inner ring failure frequency fIR=125.87Hz and 2fIR=251.74Hz. The envelope spectrum of the outer ring signal can be seen as the outer ring failure frequency that fOR=84.47Hz, 2fOR=168.94Hz, 3fOR=253.41Hz, which has a certain application value for the extraction of early weak fault characteristics of wind turbine bearings in practical engineering.
Key words: fan bearing; multi-objective beluga whale optimization(MOBWO); maximum correlated kurtosis deconvolution(MCKD); rolling bearing inner ring; bearing outer ring; envelope analysis
-
- Chinese Core Periodicals
-
- Chinese Sci-tech Core Periodicals
-
- SA, INSPEC Indexed
-
- CSA: T Indexed
-
- UPD:Indexed
-
Copyright 2010 Zhejiang Information Institute of Mechinery Industry All Rights Reserved
Technical Support：Hangzhou Bory science and technology
