基于迭代SGMD与改进MOMEDA的滚动轴承微弱故障诊断  

Weak Fault Diagnosis of Rolling Bearing Based on Iterative SGMD and Improved MOMEDA

作　　者：王富珂 高丙朋[1] 蔡鑫[1] WANG Fuke;GAO Bingpeng;CAI Xin(School of Electrical Engineering,Xinjiang University,Urumqi 830017,China)

机构地区：[1]新疆大学电气工程学院,乌鲁木齐830017

出　　处：《组合机床与自动化加工技术》2024年第12期145-150,157,共7页Modular Machine Tool & Automatic Manufacturing Technique

基　　金：国家自然科学基金项目(52065064,62303394);新疆维吾尔自治区自然科学基金项目(2022D01C694);新疆维吾尔自治区高校基本科研业务费科研项目(XJEDU2023P025)。

摘　　要：针对强背景噪声下滚动轴承故障特征微弱的问题,提出一种基于迭代辛几何模态分解(ISGMD)与改进多点最优最小熵解卷积调整(IMOMEDA)相结合的故障诊断方法。首先,利用ISGMD对故障信号进行分解并基于综合指标选取最优分量;其次,根据多点峭度谱确定MOMEDA的故障周期,利用白鹭群优化算法(ESOA)对滤波器长度进行自适应寻优,通过IMOMEDA对最优分量进行解卷积处理;最后,对解卷积处理后的信号进行包络谱分析,提取故障特征频率完成故障诊断。仿真及实验分析结果表明,所提方法能有效提取强背景噪声下的滚动轴承微弱故障特征信息。A fault diagnosis method based on iterative symplectic geometry mode decomposition(ISGMD)and improved multipoint optimal minimum entropy deconvolution adjustment(IMOMEDA)was proposed to solve the problem of weak fault characteristics of rolling bearings under strong background noise.Firstly,the fault signal is decomposed by ISGMD and the optimal component is selected based on the comprehensive index.Secondly,the fault period of MOMEDA is determined according to multipoint kurtosis spectrum,the filter length is adaptive optimized by egret swarm optimization algorithm(ESOA),and the optimal component is deconvolved by IMOMEDA.Finally,the envelope spectrum of the deconvolution signal is analyzed,and the fault characteristic frequency is extracted to complete the fault diagnosis.Simulation and experimental results show that the proposed method can effectively extract the weak fault characteristic information of rolling bearings under strong background noise.

关 键 词：滚动轴承 迭代辛几何模态分解 改进多点最优最小熵解卷积调整 综合指标 白鹭群优化算法 故障诊断 

分 类 号：TH133.33[机械工程—机械制造及自动化] TG659[金属学及工艺—金属切削加工及机床]