基于ACMD与改进MOMEDA的滚动轴承故障诊断  

A method of fault diagnosis of rolling bearings based on ACMD and improved MOMEDA

作　　者：石佳[1] 黄宇峰[1] 王锋[1] SHI Jia;HUANG Yufeng;WANG Feng(State Key Laboratory of Rail Transit Vehicle System,Southwest Jiaotong University,Chengdu 610031,China)

机构地区：[1]西南交通大学轨道交通运载系统全国重点实验室,成都610031

出　　处：《振动与冲击》2023年第16期218-226,261,共10页Journal of Vibration and Shock

基　　金：国家自然科学基金(51875481);四川省自然科学基金(2023NSFSC0370)。

摘　　要：针对强背景噪声下滚动轴承故障特征难以提取的问题,提出基于自适应非线性调频分量分解(adaptive chirp mode decomposition,ACMD)与改进多点优化最小熵解卷积(improved multipoint optimal minimum entropy deconvolution adjusted,IMOMEDA)的故障诊断方法。(1)为提高信号信噪比,采用基于基尼系数指标的ACMD,进行信号重构预处理;(2)为提高参数设定的准确性,提出改进的MOMEDA方法——利用天鹰优化算法,以多点峭度最大为目标,寻优确定滤波器周期参数;(3)对信号进行包络谱分析,通过对比包络谱的主导频率成分与理论故障特征频率,判断故障类型。仿真及实测数据分析结果表明,该方法能有效提取强背景噪声下的滚动轴承故障信号的特征信息,具备一定的优越性与实用性。As it is difficult to extract features of rolling bearings under strong background noise,a rolling bearing fault diagnosis method based on the adaptive chirp mode decomposition(ACMD)and the improved multipoint optimal minimum entropy deconvolution adjusted(IMOMEDA)was proposed.Firstly,the ACMD was integrated with a Gini index-based regrouping scheme to improve the signal-to-noise ratio.Secondly,an improved MOMEDA was proposed.In the method,the multipoint kurtosis value was used as an objective function,applying the aquila optimizer to get the optimal period parameter of MOMEDA self-adaptively for the accuracy of parameter setting.Finally,signal envelope spectrum analysis was used to determine the fault location.Simulation and analysis results of the measured data show that the proposed method can effectively extract the features of the rolling bearing fault signals under strong background noise,and has certain superiority and practicality.

关 键 词：自适应非线性调频分量分解(ACMD) 基尼系数 天鹰优化算法 多点最优调整最小熵解卷积 滚动轴承 故障诊断 

分 类 号：TH212[机械工程—机械制造及自动化] TH213.3