基于EEMD的声阵列滚动轴承故障诊断

发布时间：2018-02-28 16:00

本文关键词： 集合经验模态分解谱峭度声阵列滚动轴承故障诊断　出处：《电子测量与仪器学报》2017年09期 　论文类型：期刊论文

【摘要】：研究针对滚动轴承故障诊断中的类型和位置分析问题,提出了一种基于集合经验模态分解(EEMD)的声阵列滚动轴承故障诊断分析方法。以EEMD分解信号的峭度和能量作为评价指标,提取包含故障信息的IMF分解信号,根据滚动轴承理论故障频率及其倍频分析对分解信号进行窄带滤波后通过Hilbert包络谱实现故障类型判断,通过对窄带滤波后的分解信号使用声阵列技术进行声像分析实现滚动轴承故障定位分析。最后通过试验进行了方法验证,结果表明过使用基于EEMD分解的阵列分析方法,可更为直观确定故障位置和故障类型,有利于有轨机车等多轴承驱动系统轴承故障的快速和实时诊断,对于确定检修、制定合理维修决策、改进维修质量具有十分重要指导意义。
[Abstract]:Aiming at the problem of type and position analysis of rolling bearing fault diagnosis, a fault diagnosis and analysis method of acoustic array rolling bearing based on set empirical mode decomposition (EEMD) is proposed. The kurtosis and energy of EEMD decomposition signal are taken as evaluation indexes. The IMF decomposition signal which contains fault information is extracted. According to the fault frequency of rolling bearing theory and its frequency doubling analysis, the decomposed signal is filtered by narrow band, and the fault type is judged by Hilbert envelope spectrum. The fault location analysis of rolling bearing is realized by using acoustic array technology to analyze the decomposed signal of narrow band filter. Finally, the method is verified by experiment. The results show that the array analysis method based on EEMD decomposition is used. The fault location and fault type can be determined more intuitively, which is beneficial to the quick and real-time diagnosis of bearing fault in multi-bearing drive system such as rail locomotive. It is very important to improve the quality of maintenance.
【作者单位】：北京信息科技大学现代测控技术教育部重点实验室;
【基金】：北京教委重点(KZ201611232032) 国家自然科学基金(51575055)资助项目
【分类号】：TH133.33

【相似文献】