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田晶,王英杰,刘丽丽,张凤玲,艾延廷.基于Birge-Massart阈值降噪与EEMD及谱峭度的滚动轴承故障特征提取[J].航空动力学报,2019,34(6):1399~1408
基于Birge-Massart阈值降噪与EEMD及谱峭度的滚动轴承故障特征提取
Fault feature extraction of rolling bearing using Birge-Massart threshold denoising with EEMD and spectral kurtosis
投稿时间:2018-11-26  
DOI:10.13224/j.cnki.jasp.2019.06.023
中文关键词:  共振解调  集成经验模态分解  Birge-Massart阈值  快速谱峭度  故障诊断
英文关键词:resonance demodulation  ensemble empirical mode decomposition  Birge-Massart threshold  fast spectral kurtosis  fault diagnosis
基金项目:国家自然科学基金(11702177); 辽宁省自然科学基金(20180550650); 辽宁省教育厅项目(L201710)
作者单位
田晶 沈阳航空航天大学 辽宁省航空推进系统先进测试技术重点实验室,沈阳 110136 
王英杰 沈阳航空航天大学 辽宁省航空推进系统先进测试技术重点实验室,沈阳 110136 
刘丽丽 沈阳航空航天大学 辽宁省航空推进系统先进测试技术重点实验室,沈阳 110136 
张凤玲 沈阳航空航天大学 辽宁省航空推进系统先进测试技术重点实验室,沈阳 110136 
艾延廷 沈阳航空航天大学 辽宁省航空推进系统先进测试技术重点实验室,沈阳 110136 
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中文摘要:
      针对传统共振解调方法易受噪声干扰导致故障特征提取效果不佳的问题,提出了一种基于Birge-Massart策略的阈值降噪与集成经验模态分解(EEMD)和快速谱峭度算法相结合的滚动轴承故障特征提取方法。对原始故障信号进行EEMD并采用峭度准则筛选出含有故障信息的本征模态函数(IMF)分量;采用Birge-Massart策略和快速谱峭度对故障信号进行滤波降噪;对滤波后信号进行Hilbert包络解调,提取轴承故障特征。采用该方法分别对仿真信号和实验信号进行特征提取,结果表明该方法可以有效提高故障信号信噪比,清晰准确地获取轴承内、外圈故障的频率特征。利用峭度因子准则筛选IMF分量能有效保留原始故障信号中的冲击特征,去除无关IMF分量的影响。
英文摘要:
      For the difficulty of extracting fault feature by adopting the traditional resonance demodulation method due to noise interference, an effective fault feature extraction method of rolling bearing was proposed by integrating Birge-Massart-based threshold de-noising strategy with ensemble empirical mode decomposition (EEMD) and fast spectral kurtosis algorithm. EEMD was applied to decompose original fault signals and then extract intrinsic mode function (IMF) components containing fault information by kurtosis criterion. Birge-Massart strategy and fast spectral kurtosis were employed to filter and denoise fault signals. The fault features of the filtered signals were extracted by Hilbert envelope demodulation. Through the fault feature extraction of the simulated signals and experimental signals based on this developed method, the results demonstrate that the proposed method is effective to improve the signal-to-noise ratio of fault signals and to acquire the frequency feature of inner and outer ring faults. Using kurtosis factor criterion to select IMF components can effectively retain the impact features of the original fault signal and remove the influence of irrelevant IMF components.
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