王星河,王红军.一种WACEEMDAN和MSB的轴承故障诊断方法*[J].电子测量与仪器学报,2021,35(11):91-99
一种WACEEMDAN和MSB的轴承故障诊断方法*
Bearing fault diagnosis method based on WACEEMDAN and MSB
  
DOI:
中文关键词:  加权自适应白噪声平均总体经验模态分解  调制信号双谱  故障诊断  特征提取
英文关键词:weighted average complete ensemble empirical mode decomposition with adaptive noise  modulation signal bispectrum  fault diagnosis  feature extration
基金项目:北京市科技计划项目(Z201100008320004)、国家自然科学基金(51975058)项目资助
作者单位
王星河 北京信息科技大学机电工程学院北京100192 
王红军 1.北京信息科技大学机电工程学院北京100192;2.高端装备智能感知与控制北京市国际科技合作基地北京100192;3.现代测控技术教育部重点实验室北京100192 
AuthorInstitution
Wang Xinghe Mechanical Electrical Engineering School, Beijing Information Science & Technology University, Beijing 100192,China 
Wang Hongjun 1.Mechanical Electrical Engineering School, Beijing Information Science & Technology University, Beijing 100192,China;2.Beijing International Science Cooperation Base of Highend Equipment Intelligent Perception and Control, Beijing 100192,China;3.MOE Key Laboratory of Modern Measurement & Control Technology, Beijing 100192, China 
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中文摘要:
      针对滚动轴承发生故障时的冲击信号易被噪声淹没和其非平稳的特性,以及传统使用自适应白噪声平均总体经验模态分解(CEEMDAN)时固有模态函数(IMFs)中的有效信息不能被充分利用等问题,提出了一种基于加权自适应白噪声平均总体经验模态分解(WACEEMDAN)和调制信号双谱(MSB)的滚动轴承故障特征提取方法。首先,使用CEEMDAN将采集的非平稳振动信号分解成若干具有平稳特性的IMFs;然后,构建了一种强调敏感分量的新型指标:相关—峭度值,利用该指标对各个IMFs加权并重构为WACEEMDAN信号;最后,应用调制信号双谱(MSB)分解WACEEMDAN信号中的调制分量并提取故障特征频率。研究结果表明,通过使用西安交通大学通用轴承数据集和我们试验台进行了验证,所提出的WACEEMDAN—MSB方法能够准确的提取出轴承故障特征频率,从而验证了WACEEMDAN—MSB方法的有效性。
英文摘要:
      In view of the fact that the impact signal of rolling bearing is easily submerged by noise and non stationary characteristics, and the problem that the effective information in the IMFs cannot be fully utilized when the traditional CEEMDAN is used, a rolling bearing fault feature extraction method based on weighted average complete ensemble empirical mode decomposition with adaptive noise (WACEEMDAN) and modulation signal bispectrum (MSB) for rolling bearings is proposed. First, the CEEMDAN is used to decompose the collected non stationary vibration signals into several inherent modal functions with stationary characteristics IMFs. Then, a new type of index is constructed, which emphasizes sensitive component: correlation kurtosis value, the index is used to weight each IMFs and reconstruct it into WACEEMDAN signal. Finally, the MSB is used to decompose the modulation components in the WACEEMDAN signal and extract the fault characteristic frequency. The results show that: by using the general bearing data set of Xi’an Jiaotong University and our test bench, the proposed WACEEMDAN MSB method can accurately extract the characteristic frequency of bearing faults, thus, verify the effectiveness of the WACEEMDAN MSB method.
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