摘要
利用传统故障诊断方法对滚动轴承进行诊断时存在故障特征提取困难以及提取特征不明显的问题。针对此问题,提出了一种基于鲁棒局部均值分解(robust local mean decomposition,RLMD)以及二阶瞬态提取变换(second-order transient-extracting transform,STET)的故障特征提取方法。首先对滚动轴承故障信号进行RLMD处理,得到一系列故障信息丰富的特征分量。然后利用二阶瞬态提取变换善于提取信号中强脉冲分量的特点,对筛选出的分量进行二阶瞬态提取变换以提取脉冲故障特征进行诊断分析。实验分析结果表明,该方法能够有效地提取出故障特征,且特征提取效果优于传统诊断方法,适用于滚动轴承故障诊断。
It is difficult to extract fault features of rolling bearings by using traditional fault diagnosis methods and the extracted features are not obvious.To solve these problem,a fault feature extraction method based on robust local mean decomposition(RLMD)and the second-order transient-extracting transform(STET)was proposed.Firstly,the fault vibration signals of rolling bearing was processed by RLMD,and several components with rich fault feature information was obtained.Then using the characteristics that the second-order transient extraction transform is good at extracting the strong pulse components in the signal,the screened components are subjected to the second-order transient extraction transform to extract the pulse fault features for diagnosis and analysis.Experimental results show that the proposed method can effectively extracting fault features,the proposed method has better ability of feature extraction than the traditional diagnosis methods and is suitable for the fault diagnosis of rolling bearing.
作者
陈志刚
赵志川
钟新荣
蔡春雨
CHEN Zhi-gang;ZHAO Zhi-chuan;ZHONG Xin-rong;CAI Chun-yu(Beijing University of Civil Engineering and Architecture, Beijing 100044, China;Beijing Engineering Research Center of Monitoring for Construction Safety, Beijing 100044, China;Changqing Downhole Technology Company, CNPC Chuanqing Engineering Company, Xi'an 710021, China)
出处
《科学技术与工程》
北大核心
2022年第1期157-165,共9页
Science Technology and Engineering
基金
国家自然科学基金(51875032)
北京建筑大学市属高校基本科研业务费专项(X20061)。
关键词
鲁棒局部均值分解
二阶瞬态提取变换
滚动轴承
故障诊断
robust local mean decomposition
second-order transient-extracting transform
rolling bearing
fault diagnosis
