摘要
针对柴油发动机气门间隙故障诊断中故障特征提取难、易受噪声影响、诊断准确率低的问题,提出一种基于变分模态分解(VMD)算法和排列熵相结合的柴油机故障特征提取方法。首先,采用不同算法对仿真信号进行分解和比较分析,证明VMD方法在分解非平稳信号方面的优越性;然后,利用VMD对气门故障实测信号进行分解,并用排列熵值作为依据优化选择故障分量;最后,通过时频分析和能量分析验证所选择信号的准确性。实验结果表明,提出的方法能有效提取柴油机气门故障特征。
In order to solve the problems of difficult extraction of fault features,easy to be affected by noise and low diagnostic accuracy in value clearance fault diagnosis for diesel engines,a fault feature extraction method based on variational mode decomposition(VMD)algorithm and permutation entropy is proposed.Firstly,the superiority of VMD method in the decomposition of non-stationary signals is proved by using different algorithms to decompose and compare the simulated signals.Then,the permutation values are used as the basis to optimize the selection of fault components.Finally,the accuracy of the selected signals is verified by time-frequency analysis and energy analysis.The experimental results show that the proposed method can effectively extract the value fault features of diesel engine.
作者
王双朋
赵慧敏
梅检民
常春
万峤磊
WANG Shuangpeng;ZHAO Huimin;MEI Jianmin;CHANG Chun;WAN Qiaolei(Fifth Team of Cadets,Army Military Transportation University,Tianjin 300161,China;Military Vehicle Engineering Department,Army Military Transportation University,Tianjin 300161,China;Unit 65370,Changchun 053100,China)
出处
《军事交通学院学报》
2020年第11期46-52,共7页
Journal of Military Transportation University
关键词
气门间隙
变分模态分解(VMD)
排列熵
时频分析
valve clearance
variational mode decomposition(VMD)
permutation entropy
time-frequency analysis
作者简介
王双朋(1988—),男,硕士研究生;赵慧敏(1975—),女,博士,副教授,硕士研究生导师.
