基于VMD-MSE与SSA-SVM的往复式压缩机气阀故障诊断

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摘要往复压缩机气阀故障振动信号具有较强的非线性和非平稳性。为了从往复压缩机气阀振动信号中提取故障特征用于故障诊断，本文提出了一种基于变分模态分解（VMD）与多尺度样本熵（MSE）的故障特征提取方法，并与采用麻雀寻优算法（SSA）优化的支持向量机（SVM）相结合，用于往复压缩机气阀故障诊断。通过对往复压缩机气阀信号进行VMD分解，选取合适的内禀模态分量（IMF）进行信号重构，基于MSE熵值分析构成特征向量集，最后将其输入SSA-SVM训练并识别故障类型。实验结果表明，基于VMD-MSE与SSA-SVM的故障诊断模型能有效并准确的识别出往复压缩机气阀故障。
关键词：往复压缩机；变分模态分解；多尺度样本熵；支持向量机；模式识别

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	别锋锋1
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	朱鸿飞1
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	张莹1
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关键词 ：往复压缩机, 变分模态分解, 多尺度样本熵, 支持向量机, 模式识别

Abstract：The fault vibration signal of reciprocating compressor gas valve contains strong nonlinearity and non-stationarity. In order to extract fault features from the vibration signal of the reciprocating compressor valve for fault diagnosis, a fault feature extraction method based on Variational Mode Decomposition(VMD) and Multiscale Sample Entropy(MSE) was proposed, and the model of Support Vector Machine(SVM) optimized by Sparrow Search Algorithm(SSA) was studied for reciprocating compressor valve fault mode recognition. Through the VMD decomposition, the appropriate Intrinsic Mode Function (IMF) component of the vibration signal from the reciprocating compressor valve was selected for signal reconstruction, from which the MSE entropy value was obtained to form a feature vector set. Finally it was input into SSA-SVM for training and mode identification. Experimental study showed that the fault diagnosis model based on VMD-MSE and SSA-VMD can effectively and accurately identified the fault of the reciprocating compressor valve.
Keywords: reciprocating compressor; Variational Modal Decomposition; Multiscale Sample Entropy; Support Vector Machine; mode recognition

Key words： reciprocating compressor Variational Modal Decomposition Multiscale Sample Entropy Support Vector Machine mode recognition

收稿日期: 2021-06-02 出版日期: 2022-10-15

引用本文:

别锋锋1，2，朱鸿飞1，2，彭剑1，2，张莹1，2. 基于VMD-MSE与SSA-SVM的往复式压缩机气阀故障诊断[J]. 振动与冲击, 2022, 41(19): 289-295.
BIE Fengfeng1,2, ZHU Hongfei1,2, PENG Jian1,2, ZHANG Ying1,2. Fault diagnosis of reciprocating compressor air valve based on VMD-MSE and SSA-SVM. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(19): 289-295.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2022/V41/I19/289

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