基于频率切片小波变换时频分析的齿轮故障诊断  

GEAR FAULT DIAGNOSIS BASED ON THE FREQUENCY SLICE WAVELET TRANSFORM TIME-FREQUENCY ANALYSIS METHOD

作　　者：蔡剑华[1] 黄国玉[2] 黎小琴[1] 

机构地区：[1]湖南文理学院信息研究所,常德415000 [2]南华大学电气工程学院,衡阳421001

出　　处：《机械强度》2017年第5期1026-1030,共5页Journal of Mechanical Strength

基　　金：国家自然科学基金项目(41304098);湖南省教育厅青年项目(13B076);湖南省重点建设学科-光学基金(11ZD018);湖南文理学院博士启动项目(BSQD026)资助~~

摘　　要：为了在强噪声中有效提取齿轮的故障特征,提出了一种基于频率切片小波变换时频分析的齿轮故障诊断方法。先对信号进行频率切片小波变换,得到在全频带下的时频分布,然后在此基础上分割出含有故障特征的时频区域,再通过对该区域进行时频阈值滤波和逆变换重构分离出有效的故障特征。仿真实验和实测信号分析表明,这种方法可从噪声信号中分离出有效的特征分量,在齿轮故障诊断方面取得了较好的应用效果。In order to extract the gear fault characteristics under the strong noise condition,a fault feature separation and extraction method was proposed based on a new time-frequency decomposition method,the frequency slice wavelet transform（ FSWT）. Firstly,the signal was processed with the FSWT to get its time-frequency distribution. Then the time and frequency intervals,which contain the fault feature, were chosen to do threshold de-noising in time-frequency domain. Through reconstructing signals from the characteristic frequency slices,separation and extraction of time-frequency features were realized.The proposed method was shown to be efficient by simulations and engineering applications. It has the ability to isolate the desired components from noisy signals. It achieves an ideal effect on feature extraction for gear fault diagnosis.

关 键 词：频率切片小波变换 时频分析 齿轮 故障诊断 

分 类 号：TH17[机械工程—机械制造及自动化] TN911.7[电子电信—通信与信息系统]