CSES应用SVMD-Teager Energy的滚动轴承多工况下故障诊断方法  

CSES Application of SVMD Teger Energy for Fault Diagnosis of Rolling Bearings under Multiple Operating Conditions

作　　者：李金城 张凯 LI Jin-cheng;ZHANG Kai(College of Information Engineering,Shenyang Chemical University,Shenyang 110142 China;Equipment Reliability Research Institute,Shenyang Chemical University,Shenyang 110142 China)

机构地区：[1]沈阳化工大学信息工程学院,辽宁沈阳110142 [2]沈阳化工大学装备可靠性研究所,辽宁沈阳110142

出　　处：《自动化技术与应用》2025年第2期1-3,65,共4页Techniques of Automation and Applications

基　　金：国家重点研发计划项目(2019YFB2004401);辽宁省高端人才建设工程-辽宁省特聘教授([2018]3533号)资助;辽宁省教育厅重点(普通)项目(LJKZ0435)。

摘　　要：针对多工况下的滚动轴承复合故障信号易被高噪声淹没而难以提取之难点,提出使用联合平方包络谱(CSES)对滚动轴承进行故障频率判定,但被高噪声包围的复合故障振动信号使得谱分析效果大打折扣。首先信号预处理采用的连续变分模态分解(SVMD)克服人为设置分解模态K数,避免了模态混叠,减少噪声干扰,依据相关系数提高重构信号精度;其次,采用Teager能量算子突显瞬时故障脉冲特征;最后,采用CSES原理,计算滤波后信号的平方包络谱。通过仿真与实验,在不同载荷情况下,与快速包络谱、CSES相比,该方法能够有效提取滚动轴承在多工况下初期的故障信号。Faced with the difficulty that the composite error signal of the rolling bearing under multiple working conditions is easily flooded by high noise and difficult to extract,it is proposed to use the Combined Squared Envelope Spectrum(CSES)to determine the fail-ure rate of rolling bearings,however,the composite error vibration signal surrounded by strong noise significantly reduces the ef-fect of spectral analysis.The specific application process can be found here.First,the SVMD used in signal preprocessing over-comes the artificial adjustment of the decomposition mode K number,avoids mode aliasing,reduces noise interference,and im-proves the accuracy of the reconstructed signal according to the correlation coefficient.Second,the Teager energy operator is used to highlight the properties of transient error pulses.Finally,the squared envelope spectrum of the filtered signal is calculated accord-ing to the CSES principle.Through simulation and experiment under different load conditions,compared to Fast Envelope Spec-trum,CSES,this method can effectively extract the initial error signal of the rolling bearing under multiple working conditions.

关 键 词：滚动轴承 多载荷 信号处理 故障诊断 降噪 故障特征 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置] TH133.33[自动化与计算机技术—控制科学与工程]