基于改进小波去噪预处理和EEMD的采煤机齿轮箱故障诊断  

Shearer gearbox fault diagnosis based on improved wavelet denoising pretreatment and EEMD

作　　者：李力[1,2,3] 倪松松 LI Lil, NI Songsong(School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China State Key Laboratory of High Performance Complex Manufacturing, Changsha 410083, China State Key Laboratory of Deep Sea Mineral Resources Development and Utilization Technology, Changsha 410012, China)

机构地区：[1]中南大学机电工程学院,湖南长沙410083 [2]高性能复杂制造国家重点实验室,湖南长沙410083 [3]深海矿产资源开发利用技术国家重点实验室,湖南长沙410012

出　　处：《中南大学学报（自然科学版）》2016年第10期3394-3400,共7页Journal of Central South University:Science and Technology

基　　金：国家重点基础研究发展计划(973计划)项目(2014CB046305);国家大洋专项项目(DY125-14-T-03)~~

摘　　要：针对采煤现场强噪声背景下采煤机齿轮箱振动信号集合经验模态分解(EEMD)故障特征不明显和分解效率较低的问题,提出基于改进小波去噪预处理和EEMD的故障诊断方法。采用小波改进阈值函数法对振动信号进行去噪预处理,与传统小波阈值函数法相比能够有效地提高信号的信噪比。对去噪后的信号进行EEMD分解得到若干个本征模态分量(IMF),计算各IMF分量的相关度并剔除虚假分量。将该方法应用于采煤机齿轮箱行星轮的故障诊断,通过对真实的IMF分量进行频谱分析并提取信号的故障特征频率,与未去噪的信号进行对比。研究结果表明:该方法能够突出故障特征频率,使分解效率提高17.35%,并能进一步减小模态混叠现象。In strong noise background of the coal mining, the fault features of shearer gearbox vibration signal ensemble empirical mode decomposition（EEMD） was not obvious, and the decomposition was inefficient, for which a method based on the improved wavelet denoising pretreatment and EEMD was presented. The original signal was denoised by the method of wavelet improved threshold function; the signal-to-noise ratio was improved effectively compared to traditional threshold function method.The denoised signal was decomposed into several intrinsic mode functions（IMFs） by EEMD. The relevance of IMFs were analyzed to get rid of the illusive components of decomposition results. This method was applied in the shearer gearbox planetary gear fault diagnosis, and the fault characteristic frequency of denoised signal was extracted by the spectral analysis method in useful IMFs. The experimental results were compared with the analysis results of the original signal. The results show that the proposed method can make the fault features more distinct and improve decomposition efficiency by 17.35%, and further reduce the modal mixing problem.

关 键 词：采煤机齿轮箱 故障特征 分解效率 改进小波去噪 集合经验模态分解 行星轮 模态混叠 

分 类 号：TH165.3[机械工程—机械制造及自动化] TD421[矿业工程—矿山机电]