首页|基于SSA-VMD和SVM的滚珠丝杠副故障诊断

基于SSA-VMD和SVM的滚珠丝杠副故障诊断

扫码查看
针对滚珠丝杠副故障特征提取困难的问题,提出一种基于麻雀搜索算法优化变分模态分解算法(SSA-VMD)结合支持向量机(SVM)的滚珠丝杠副故障诊断方法.以最小包络熵作为SSA的适应度函数,对VMD参数进行自主寻优;运用IMF能量值对分解信号进行筛选重构,去除噪声和无关成分的干扰;最后提取重构信号的8类时域特征参数和5类频域特征参数作为特征向量集,导人SVM进行故障识别模型的训练.通过搭建滚珠丝杠副故障诊断实验平台采集振动信号,分别采用SSA-VMD、VMD、EMD方法进行信号分解提取故障特征.实验结果表明:与VMD和EMD相比,SSA-VMD能针对不同的信号自主选择最优的VMD参数进行信号分解,能准确识别滚珠丝杠副故障类型,证明了基于SSA-VMD的滚珠丝杠副故障诊断的可行性和准确性.
Fault Diagnosis of Ball Screw Pair Based on SSA-VMD and SVM
A ball screw pair fault diagnosis method based on sparrow search algorithm optimized variational modal decomposition algorithm(SSA-VMD)combined with support vector machine(SVM)was proposed for the problem of difficult extraction of ball screw pair fault features.The autonomous optimization of the VMD parameters was performed using the minimum envelope entropy as the fit-ness function of the SSA.The IMF energy value was used to filter and reconstruct the decomposed signal to remove the noise and irrele-vant components.Finally,8 types of time-domain feature parameters and 5 types of frequency-domain feature parameters of the recon-structed signal were extracted as the feature vector set,which were imported into SVM for training the fault recognition model.Vibration signals were collected by building a ball screw pair fault diagnosis experiment platform.The SSA-VMD,VMD,and EMD methods were used to decompose the signals and extract the fault features,respectively.The experimental results show that compared with VMD and EMD,SSA-VMD can autonomously select the optimal VMD parameters for signal decomposition with different signals,and can accu-rately identify the type of ball screw pair fault.The results demonstrate the feasibility and accuracy of SSA-VMD-based ball screw pair fault diagnosis.

ball screw pairvariational modal decomposition(VMD)sparrow search algorithm(SSA)fault diagnosis

左乾君、陈国华、毛杰、张帅伟、周博、张智洋、张秀琴

展开 >

湖北文理学院机械工程学院,湖北襄阳 441053

重庆渝江压铸股份有限公司,重庆 401123

滚珠丝杠副 变分模态分解(VMD) 麻雀搜索算法(SSA) 故障诊断

2022年湖北省自然科学基金创新发展联合基金2021年襄阳市重点科技计划项目2022年襄阳市科技计划项目

2022CFD081襄科计[2021]10号2022ABH006436

2024

机床与液压
中国机械工程学会 广州机械科学研究院有限公司

机床与液压

CSTPCD北大核心
影响因子:0.32
ISSN:1001-3881
年,卷(期):2024.52(12)
  • 10