机电工程技术2025，Vol.54Issue(16)：19-23,68,6.DOI:10.3969/j.issn.1009-9492.2024.00162
基于AO-VMD和RCMFE的往复压缩机气阀故障诊断方法
Fault Diagnosis Method of Reciprocating Compressor Valve Based on AO-VMD and RCMFE
摘要
Abstract
In response to the problem of insufficient signal decomposition caused by difficulty in manually selecting parameters in the variational mode decomposition(VMD)method,a new method of reciprocating compressor valve signal decomposition optimized by the aquila optimizer(AO)for VMD is proposed.The decomposition scale K and penalty factor α of VMD and output the optimal values are optimized,and then the original signal is decomposes and reconstructed.Then,the refined composite multi-scale fuzzy entropy(RCMFE)algorithm is used to calculate the entropy values of each component for signal feature extraction,and a suitable set of fault feature vectors is constructed.Finally,the BP neural network algorithm is used to identify and diagnose faults in different states of the air valve.The diagnostic results show that compared with traditional methods,the overall fault recognition rate of this method reaches 94.2%,has achieved a good improvement in the accuracy of fault diagnosis and has good superiority in fault feature extraction.关键词
气阀故障诊断/天鹰算法/变分模态分解/精细复合多尺度模糊熵/BP神经网络Key words
valve fault diagnosis/aquila optimizer/variational mode decomposition/fine composite multi-scale fuzzy entropy/BP neural network分类
机械制造引用本文复制引用
张善帅,张秀珩,张吉涛,林鑫泉..基于AO-VMD和RCMFE的往复压缩机气阀故障诊断方法[J].机电工程技术,2025,54(16):19-23,68,6.基金项目
国家自然科学基金(51934002) （51934002）
