基于ICEEMDAN-MPE和AO-LSSVM的滚动轴承故障诊断  

Rolling bearing fault diagnosis based on ICEEMDAN-MPE and AO-LSSVM

作　　者：李铭 何毅斌[1] 马东 唐权 胡明涛 Li Ming;He Yibin;Ma Dong;Tang Quan;Hu Mingtao(School of Mechanical and Electrical Engineering,Wuhan Institute of Technology,Wuhan 430205,China)

机构地区：[1]武汉工程大学机电工程学院,武汉430205

出　　处：《电子测量技术》2022年第23期66-71,共6页Electronic Measurement Technology

基　　金：湖北省科技厅重大专项(2016AAA056);化工装备强化与本质安全湖北省重点实验室开放基金(2018KA01);武汉工程大学第十三届研究生教育创新基金(CX2021051)项目资助。

摘　　要：针对滚动轴承故障诊断中特征提取困难和故障类型识别准确率偏低等情况,提出一种基于改进型自适应噪声完整集成经验模态分解(ICEEMDAN)与多尺度排列熵(MPE)结合天鹰算法(AO)优化最小二乘支持向量机(LSSVM)正则化参数和核参数的故障诊断方法。首先通过ICEEMDAN对轴承原始振动信号进行分解,其次根据相关系数和方差贡献率双原则选取符合标准的本征模态分量(IMF),并计算对应分量的MPE,以全面获取故障特征信息;最后将其构成多维特征向量,利用AO-LSSVM辨识模型实现对轴承故障诊断。同时进行多组对比实验,研究结果表明了所提方法在滚动轴承故障诊断中的优越性且识别准确率可达98.95%。In view of the difficulty of feature extraction and the low accuracy of fault type recognition in rolling bearing fault diagnosis, a fault diagnosis method based on Improved Complete Ensemble Empirical Mode Decomposition with adaptive noise(ICEEMDAN) and Multi-scale Permutation Entropy(MPE) combined with Aquila Optimizer(AO) to optimize the regularization parameters and kernel parameters of Least Squares Support Vector Machine(LSSVM) is proposed. Firstly, the original vibration signal of the bearing is decomposed by ICEEMDAN. Secondly, according to the double principles of correlation coefficient and variance contribution rate, the eigenmode component(IMF) that meets the standard is selected, and the MPE of the corresponding component is calculated to comprehensively obtain the fault characteristic information;Finally, the multi-dimensional feature vector is formed, and the bearing fault diagnosis is realized by using AO-LSSVM identification model. At the same time, several groups of comparative experiments are carried out. The results show the superiority of the proposed method in rolling bearing fault diagnosis, and the recognition accuracy can reach 98.95%.

关 键 词：故障诊断 ICEEMDAN 多尺度排列熵 天鹰算法 最小二乘支持向量机 

分 类 号：TH133.33[机械工程—机械制造及自动化]