噪声与振动控制Issue(4)：129-132,4.DOI:10.3969/j.issn.1006-1335.2015.04.029
基于MED和循环域解调的多故障特征提取
Multi-fault Diagnosis Based on MED and Cyclic Autocorrelation Function
摘要
Abstract
The fault characteristic signals of gearboxes are usually drowned out in strong background noise. So, it is difficult to identify them by using conventional diagnosis methods. In this paper, the method combining MED (minimum entropy deconvolution) with cyclic autocorrelation function was proposed to extract the fault features of the gearboxes. It is found that the cyclic autocorrelation function can be applied to separate out the modulators effectively, but it does not work well in the condition of very strong background noise. Thus, the MED was used as the pre-filter to refine the vibration signals with the maximum steepness value as the ultimate condition of filtering, so that the interference of the strong background noise was eliminated and the fault feature signals could be extracted. The strong denoising function of this method was verified through simulative signals. Finally, this method was applied to the denoise processing of multi-fault vibration signals of a turbine gearbox in a strong noise background. The denoised signals were analyzed with the cyclic autocorrelation function. The fault features of the signals were successfully extracted. The reliability and feasibility of this method were verified.关键词
振动与波/齿轮箱/最小熵反褶积/循环自相关函数/故障诊断/多故障Key words
vibration and wave/gearbox/MED/cyclic autocorrelation function/fault diagnosis/multiple faults分类
信息技术与安全科学引用本文复制引用
王志坚,韩振南,宁少慧,李延峰..基于MED和循环域解调的多故障特征提取[J].噪声与振动控制,2015,(4):129-132,4.基金项目
国家自然基金（50775157）；山西省基础研究项目(2012011012-1) （2012011012-1）
