摘要
针对双树复小波变换存在频率混叠以及参数需自定义的缺陷,提出自适应改进双树复小波变换的齿轮箱故障诊断方法。首先,利用双树复小波变换将信号进行分解和单支重构,采用粒子群算法将分解后分量峭度值作为适应度函数,选择双树复小波的最优分解层数;其次,对重构出的低频信号进行频谱分析提取故障特征,将单支重构后的各高频分量进行变分模态分解,通过峭度值获得各高频分量经变分模态分解后的主频率分量信号;最后,分析各主频率分量信号的频谱,识别齿轮箱的故障特征。结果表明,该方法与双树复小波变换和变分模态分解相比,不仅消除了频率混叠现象,提高了信噪比和频带选择的正确性,而且还提高了从强噪声环境中提取瞬态冲击特征的能力。
In the light of frequency aliasing and parameter custom caused by doubletree complex wavelet transform,a fault diagnosis method of adaptive improved dual-tree complex wavelet transform is proposed.This method integrates dual-tree complex wavelet transform-variational mode decomposition(DTCWTVMD).First,the signal is decomposed and reconstructed by dual-tree complex wavelet transform.Particle swarm optimization(PSO)is used to determine the component kurtosis value as a fitness function to select the optimal decomposition level of doubletree complex wavelet.Second,the reconstructed low-frequency signal is subjected to spectrum analysis to extract the fault characteristic signal.The high-frequency components are reconstructed by variational mode decomposition,and through the kurtosis value,the main frequency component signal of each high-frequency component decomposed by variational mode is obtained.Finally,the spectrum of the main frequency component signals is analyzed to identify the fault fre-quency of the gearbox.The experimental results show that the proposed method eliminates frequency aliasing and improves the correctness of signal-to-noise ratio and frequency band selection compared with that process by the dual-tree complex wavelet transform and variational mode decomposition.Besides,it improves the ability to extract transient shock characteristics from a strong noisy environment.
作者
陈旭阳
韩振南
宁少慧
CHEN Xuyang;HAN Zhennan;NING Shaohui(School of Mechanical Engineering, Taiyuan University of Technology Taiyuan,030024, China;School of Mechanical Engineering, Taiyuan Universiy of Science and Technology Taiyuan,030024, China)
出处
《振动．测试与诊断》
EI
CSCD
北大核心
2019年第5期1016-1022,1133,1134,共9页
Journal of Vibration,Measurement & Diagnosis
基金
国家自然科学基金资助项目（50775157）
山西省基础研究资助项目（2012011012-1）
山西省高等学校留学回国人员科研资助项目（2011-12）
关键词
双树复小波变换
粒子群优化
变分模态分解
峭度值
齿轮箱
故障诊断
dual-tree complex wavelet transform
particle swarm optimization
variational mode decomposition
kurtosis value
gearbox
fault diagnosis
