基于VMD与自适应MOMEDA的回转支承故障诊断 郑强.pdf

文档介绍：第 5 期 组合机床与自动化加工技术 No． 5
2022 年 5 ， ， ， �
ical Engineering and Automation Fuzhou University Fuzhou 350108 China
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Abstract Aiming at the problem that it is difficult to extract the fault characteristics of the low -speed and
，
heavy-load slewing bearing under strong background noise a slewing bearing fault diagnosis method based
on the combination of variational mode decomposition and multipoint optimal minimum entropy deconvolu-
， ，
tion adjusted is proposed． Firstly the VMD algorithm is used to decompose the original vibration signal and
，
the optimal component of kurtosis is selected from it． Secondly the parameter T in the MOMEDA algorithm
，
is optimized by the grey wolf optimization algorithm and then the fault impact component of the optimal