马超,王少红,徐小力.基于EEMD的声阵列滚动轴承故障诊断[J].电子测量与仪器学报,2017,31(9):1379-1384
基于EEMD的声阵列滚动轴承故障诊断
Fault diagnosis for rolling bearing by using acoustic array based on EEMD
  
DOI:10.13382/j.jemi.2017.09.005
中文关键词:  集合经验模态分解  谱峭度  声阵列  滚动轴承  故障诊断
英文关键词:EEMD  kurtosis  acoustic array  rolling bearing  fault diagnosis
基金项目:北京教委重点(KZ201611232032)、国家自然科学基金(51575055)资助项目
作者单位
马超 北京信息科技大学现代测控技术教育部重点实验室北京100192 
王少红 北京信息科技大学现代测控技术教育部重点实验室北京100192 
徐小力 北京信息科技大学现代测控技术教育部重点实验室北京100192 
AuthorInstitution
Ma Chao Key laboratory of Modern Measurement and Control Technology Ministry of Education, Beijing Information Science and Technology University, Beijing 100192,China 
Wang Shaohong Key laboratory of Modern Measurement and Control Technology Ministry of Education, Beijing Information Science and Technology University, Beijing 100192,China 
Xu Xiaoli Key laboratory of Modern Measurement and Control Technology Ministry of Education, Beijing Information Science and Technology University, Beijing 100192,China 
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中文摘要:
      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。
英文摘要:
      For further study about diagnosis of the fault type and location forthe rolling bearing, the method by using the acoustic array signals analysis with EEMD decomposition is proposed.Based on the kurtosis and power index values, the EEMD decomposition is carried out and the IMF component including faults information is extracted. After computing the theoretical fault frequency and the harmonics of the bearing’s components, the narrow band filter is used for the extracted IMF component and Hilbert transform is done consciously for envelope spectrum, which is used to determine the fault type. Also the extracted IMF components filtered with narrow band filter for each acoustic array signals are used as input signals for the acoustical image analysis to the fault location. Finally, the verified experiments are carried out and results showed thatby using this method the diagnosis could be more intuitive to determine the fault location and fault types, which is better forthe bearing fault determination of the drive system, the maintenance and reasonable maintenance decision and improving the service quality.
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