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,提出一种基于变分模态分解(variational mode decomposition,VMD)与多尺度熵(multi-scale entrope,MSE)的故障特征提取方法,并与采用麻雀寻优算法(soarrow search algorithm,SSA)优化的支持向量机(suppot vector mackine,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(intrinsic mode function,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。试验结果表明,基于VMD-MSE与SSA-SVM的故障诊断模型能有效并准确的识别往复压缩机气阀故障。
Fault vibration signals of reciprocating compressor air valve have strong nonlinearity and non-stationarity.Here,to extract fault features from reciprocating compressor air valve vibration signals for fault diagnosis,a fault feature extraction method based on variational modal decomposition(VMD)and multi-scale sample entropy(MSE)was proposed,and it was combined with support vector machine(SVM)optimized using sparrow search algorithm(SSA)for reciprocating compressor air valve fault diagnosis.After reciprocating compressor air valve signal was decomposed with VMD,appropriate intrinsic mode functions(IMFs)were chosen for signal reconstruction,and feature vector set was formed based on MSE analysis.Finally,feature vector set was input into SSA-SVM for training and identifying fault types.The experimental results showed that the proposed fault diagnosis model based on VMD-MSE and SSA-SVM can effectively and correctly identify reciprocating compressor air valve faults.
作者
别锋锋
朱鸿飞
彭剑
张莹
BIE Fengfeng;ZHU Hongfei;PENG Jian;ZHANG Ying(College of Mechanical and Rail Transit Engineering,Changzhou University,Changzhou 213164,China;Jiangsu Provincial Key Lab of Green Process Equipment,Changzhou University,Changzhou 213164,China)
出处
《振动与冲击》
EI
CSCD
北大核心
2022年第19期289-295,共7页
Journal of Vibration and Shock
基金
国家自然科学基金(52075050)
江苏省教育厅自实然科学重大项目(19KJA430004)
江苏省研究生实践创新项目(SJCX20_1006)。
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
reciprocating compressor
variational modal decomposition(VMD)
multiscale sample entropy(MSE)
support vector machine(SVM)
sparrow search algorithm(SSA)
mode recognition