基于蝙蝠算法的VMD在滚动轴承故障诊断中的应用  

Application of VMD Based on Bat Algorithm in Rolling Bearing Fault Diagnosis

作　　者：张杰[1] 齐明思[1] Zhang Jie;Qi Mingsi(North University of China,Taiyuan 030051,China)

机构地区：[1]中北大学,太原030051

出　　处：《煤矿机械》2022年第1期164-166,共3页Coal Mine Machinery

基　　金：国家自然科学基金(51905046)。

摘　　要：由于滚动轴承的工作情况较为复杂,信噪比较低,使用传统的故障处理方法难以对信号的故障进行有效识别。提出了一种新的故障诊断方法,基于蝙蝠算法提出了参数自适应的变分模态分解(VMD)优化算法。在该方法中,使用蝙蝠算法依据故障的相关分量包络谱熵最小原则获得VMD分量的优化值,实现故障的识别。通过实验验证,该方法能够有效地提取滚动轴承的故障特征,具有一定的实用价值。Because the working condition of rolling bearing is complex and the signal-to-noise ratio is low, it is difficult to effectively identify the signal fault by using the traditional fault processing method.A new fault diagnosis method was proposed. The parameter adaptive variational mode decomposition(VMD) optimization algorithm based on bat algorithm was proposed. In this method, bat algorithm is used to obtain the optimal value of VMD component according to the principle of minimum envelope spectral entropy of fault correlation component, so as to realize fault identification. Through the experimental verification, the method can effectively extract the fault characteristics of rolling bearing and has certain practical value.

关 键 词：滚动轴承 蝙蝠算法 VMD 

分 类 号：TH133.33[机械工程—机械制造及自动化]