基于三域特征提取和WOA-ELM的滚动轴承故障诊断  

Rolling bearing fault diagnosis based on three-domain feature extraction and WOA-ELM

作　　者：耿建平[1] 陈志炜 GENG Jianping;CHEN Zhiwei(School of Electronic Engineering and Automation,Guilin University of Electronic Technology,Guilin 541004,China)

机构地区：[1]桂林电子科技大学电子工程与自动化学院,广西桂林541004

出　　处：《桂林电子科技大学学报》2022年第6期456-462,共7页Journal of Guilin University of Electronic Technology

摘　　要：为提高滚动轴承故障诊断率,提出一种基于三域特征提取和鲸鱼算法优化极限学习机(WOA-ELM)的滚动轴承故障诊断方法。首先,分别对滚动轴承的振动信号进行时域分析、频谱分析和小波包分解,构成故障特征向量集;其次,为避免维度过高影响后续模型诊断效率,应用流形学习中的局部保留投影对特征向量集进行降维并剔除冗余特征,获得对故障信息更具针对性的特征向量集;为解决极限学习机易陷入局部最优的问题,引入鲸鱼算法对极限学习机网络参数进行迭代寻优,提高ELM网络性能;最后,建立鲸鱼算法优化极限学习机的滚动轴承诊断模型,对故障进行分类和诊断。采用美国凯斯西储大学轴承数据对WOA-ELM进行训练和测试,实验结果表明,该方法能有效提高滚动轴承故障诊断率。In order to effectively raise the fault detection accuracy of rolling bearing,a method is proposed in extracting different domains′feature and optimizing extreme learning machine(ELM)by whale algorithm.Firstly,time domain analysis,spectrum analysis and wavelet packet decomposition are used in the process of the vibration signal.Secondly,in order to avoid dimensional disasters,locality preserving projection in manifold learning is applied to reduce the dimensions of mixed feature sets and eliminate redundant features;In order to ameliorate the deficiency that ELM is prone to come to local optimal value,whale optimization algorithm is used to adjust the parameters of network;Lastly,WOA-ELM rolling bearing diagnosis model is established to classify and diagnose faults.Using the bearing data from Western Reserve University to simulate.The result of the test shows that this method can usefully increase rate accuracy of fault diagnosis.

关 键 词：轴承故障诊断 三域特征 局部保留投影 鲸鱼算法 极限学习机 

分 类 号：TH133[机械工程—机械制造及自动化]