摘要
针对齿轮箱多级齿轮传动振动信号易受噪声干扰,低频微弱故障特征提取难的问题,提出一种最大重叠离散小波包变换(MODWPT)和Choi-Williams分布(CWD)相结合的齿轮低频故障诊断方法。首先采用MODWPT方法将复杂的振动信号分解为若干分量,避免了经验模态分解(EMD)存在的模态混叠和端点效应等问题,然后依据峭度准则筛选合适分量,最后将选取的分量进行CWD分析,将时频谱表现出的频率特征与理论故障特征频率对比,识别出齿轮故障特征,实现故障诊断。通过齿轮故障的仿真及实验研究,说明了该方法较传统EMD-WVD方法的优越性,同时验证了该方法的有效性和可行性。
Aiming at the problem that the vibration signal of gearbox multi-stage gear transmission is easy to be disturbed by noise and the feature extraction of low-frequency weak fault is difficult,a low-frequency fault diagnosis method is proposed by combining the maximum overlap discrete wavelet packet transform(MODWPT)and Choi Williams distribution(CWD).Firstly,the complex vibration signal is decomposed into several components by modwpt,which avoids the problems of modal confusion and end-point effect existing in empirical mode decomposition(EMD).Then,the appropriate components are selected according to kurtosis criterion.Finally,the selected components are analyzed by CWD,and the frequency characteristics of time-frequency spectrum are compared with the theoretical fault characteristics to identify the fault characteristics of gear,so as to achieve the goal Disability diagnosis.Through the simulation and experimental research of gear fault,the superiority of this method compared with the traditional EMDWVD method is illustrated,and the validity and feasibility of this method are verified.
作者
刘奇
荆双喜
冷军发
罗晨旭
LIU Qi;JING Shuangxi;LENG Junfa;LUO Chenxu(School of Mechanical and Power Engineering,Henan Polytechnic University,Jiaozuo Henan 454000,China)
出处
《机械设计与研究》
CSCD
北大核心
2020年第5期96-100,共5页
Machine Design And Research
基金
河南省高等学校重点科研项目,采煤机智能故障诊断(19A440007)
河南理工大学博士基金项目,低速齿轮箱复合故障诊断技术研究(B2017-28)
作者简介
刘奇(1993-),男,硕士研究生,主要研究方向:机械设备状态监测与故障诊断,已发表论文1篇,E-mail:15538939601@163.com。
