基于RCMDE与极限学习机的滚动轴承故障诊断  

Rolling bearing fault diagnosis based on RCMDE and extreme learning machine

作　　者：刘云斌[1] 钱俊[2] 潘曙明[1] LIU Yunbin;QIAN Jun;PAN Shuming(Jinhua Technician College,Jinhua 321017,CHN;Zhejiang University,Hangzhou 310000,CHN)

机构地区：[1]金华市技师学院,浙江金华321017 [2]浙江大学,浙江杭州310000

出　　处：《制造技术与机床》2023年第2期123-126,共4页Manufacturing Technology & Machine Tool

基　　金：国家自然科学基金(52075477)。

摘　　要：针对滚动轴承故障信号识别率低的情况,提出一种基于精细复合多尺度离散熵(RCMDE)与极限学习机(ELM)的故障诊断方法。首先,从原始振动信号中提取20个尺度的精细复合多尺度离散熵并以此构建故障特征集,然后利用ELM对其进行故障种类识别。通过凯斯西储大学的轴承数据验证提出方法的有效性,最后将提出方法与MPE-ELM进行对比。对比结果说明提出的故障诊断方法具有更高的分类精度。Aiming at the low recognition rate of rolling bearing fault signals,a fault diagnosis method based on refined composite multiscale dispersion entropy(RCMDE)and extreme learning machine(ELM)is proposed.Firstly,extracting 20 scales of refined composite multi-scale discrete entropy from the original vibration signal and constructing a fault feature set based on this,and then using ELM to identify fault types.The validity of the proposed method was verified by bearing data from Case Western Reserve University.Finally,the proposed method is compared with MPE-ELM.The comparison results show that the proposed fault diagnosis method has higher classification accuracy.

关 键 词：精细复合多尺度离散熵 ELM 故障诊断 

分 类 号：TH165.3[机械工程—机械制造及自动化]