摘要
针对奇异值分解(SVD)优化局部均值分解(LMD)方法提取工业机器人交叉滚子轴承振动信号微弱故障特征分量时出现的模态混淆现象,提出一种基于最大分辨率SVD与LMD的工业机器人交叉滚子轴承故障特征提取方法。以最大奇异值分辨率原则将一维振动信号构造成Hankel矩阵,采用SVD对Hankel矩阵进行分解得到奇异值序列;按照奇异值曲率谱原则及非目标值抑制原则对奇异值序列进行重构,将包含故障突变信息的重构奇异值序列进行SVD逆运算得到重构振动信号;最后利用LMD方法对重构振动信号进行故障特征提取,得到能够表征原始振动信号振动特征的故障特征分量。通过与SVD优化LMD方法对比可知,该方法完整地提取了工业机器人交叉滚子轴承振动信号的微弱故障特征分量,改善了模态混淆现象。
Aiming at the mode confusion phenomenon of singular value decomposition(SVD)optimizing local mean decomposition(LMD)extracting the weak fault feature component from the cross roller bearings for industrial robots,a method for fault feature extraction of industrial robot cross roller bearings based on maximum resolution SVD and LMD was proposed.One dimensional vibration signal was constructed into Hankel matrix based on the maximum singular value resolution principle,the Hankel matrix was decomposed by SVD to obtain the sequence of singular values.The singular value sequence was reconstructed according to the principle of singular value curvature spectrum and non-target value suppression principle,the reconstructed vibration signal was obtained by SVD reverse operation of the reconstructed singular value sequence containing the main fault information.Finally,LMD method was used to extract fault features from reconstructed signals,the fault feature component which could highlight the vibration feature of the original signal was obtained.Compared with SVD optimized LMD method,using the designed method,the weak fault characteristic components of the industrial robot cross roller bearing vibration signal are extracted completely and the mode confusion is improved.
作者
何冬康
刘方平
谭顺学
和杰
舒凡
HE Dongkang;LIU Fangping;TAN Shunxue;HE Jie;SHU Fan(Liuzhou Vocational&Technical College,Liuzhou Guangxi 545006,China;Special Equipment Safety Inspection Institute in Yunnan Province,Kunming Yunnan 650034,China;Yunnan Huimin Labor Service Limited Company,Kunming Yunnan 650032,China)
出处
《机床与液压》
北大核心
2023年第4期191-196,共6页
Machine Tool & Hydraulics
基金
广西高校中青年教师基础能力提升项目(2018KY0986)
柳州职业技术学院2022年校级科研项目(2022KB04)。
关键词
交叉滚子轴承
奇异值分解
局部均值分解
特征提取
Cross roller bearing
Singular value decomposition
Local mean decomposition
Feature extraction
