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基于参数优化VMD和样本熵的滚动轴承故障诊断
刘建昌; 权贺; 于霞; 何侃; 李镇华
Source Publication自动化学报
ISSN0254-4156
2022
Volume48Issue:3Pages:808-819
Abstract针对滚动轴承故障特征提取不丰富而导致的诊断识别率低的情况, 提出了基于参数优化变分模态分解(Variational mode decomposition, VMD)和样本熵的特征提取方法, 采用支持向量机(Support vector machine, SVM)进行故障识别. VMD方法的分解效果受限于分解个数和惩罚因子的选取, 本文分析了这两个影响参数选取的不规律性, 采用遗传变异粒子群算法进行参数优化, 利用参数优化的VMD方法处理故障信号. 样本熵在衡量滚动轴承振动信号的复杂度时, 得到的熵值并不总是和信号的复杂度相关, 故结合滚动轴承的故障机理, 提出基于滚动轴承故障机理的样本熵, 此样本熵衡量振动信号的复杂度与机理分析的结果一致. 仿真实验表明, 利用本文提出的特征提取方法, 滚动轴承的故障诊断准确率有明显的提高.
Keyword变分模态分解 参数优化 遗传变异粒子群 样本熵 故障诊断
DOI10.16383/j.aas.c190345
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/56391
Collection学术期刊_自动化学报
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GB/T 7714
刘建昌,权贺,于霞,等. 基于参数优化VMD和样本熵的滚动轴承故障诊断[J]. 自动化学报,2022,48(3):808-819.
APA 刘建昌,权贺,于霞,何侃,&李镇华.(2022).基于参数优化VMD和样本熵的滚动轴承故障诊断.自动化学报,48(3),808-819.
MLA 刘建昌,et al."基于参数优化VMD和样本熵的滚动轴承故障诊断".自动化学报 48.3(2022):808-819.
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