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MBCV-EWT和奇异值差分谱的滚动轴承信号降噪方法
Rolling Bearing Vibration Signal De-noising Method Based on MBCV-EWT and Singular Value Difference Spectrum
  
DOI:10.16450/j.cnki.issn.1004-6801.2019.04.021
中文关键词:  信号降噪  最大类间方差-经验小波变换分解  奇异值差分谱  滚动轴承
英文关键词:signal de-noising  maximum between-cluster variance-empirical wavelet transform (MBCV-EWT)  singular value difference spectrum  rolling bearing
基金项目:(国家自然科学基金资助项目(51575143);黑龙江省自然科学基金资助项目(E2016046)
作者单位
王亚萍,崔巍,葛江华,许迪,李云飞 (哈尔滨理工大学机械动力工程学院 哈尔滨150080) 
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中文摘要:
      针对滚动轴承振动信号降噪时,克服模式混叠、保证各频率成分完整性和独立性问题,提出最大类间方差-经验小波变换分解(maximum between-cluster variance-empirical wavelet transform,简称MBCV-EWT)与奇异值差分谱相结合的信号降噪方法。首先,针对传统区间划分的不确定性问题,提出MBCVEWT信号分解方法,通过最大类间方差对信号频谱自适应划分,并在每个划分区间上构建带通滤波器;其次,针对分解分量冗余,提出脉冲指标作为调幅-调频分量筛选准则,选取最优的分量用于降噪;最后,对最优调幅-调频分量进行奇异值分解,根据其差分谱重构分量并实现降噪。仿真及实验结果表明,该方法能够实现频谱自适应划分,有效克服模式混叠等问题,保证分解得到的各成分主频独立且完整,调幅-调频分量筛选准确,降噪效果明显,为故障识别和预测奠定研究基础。
英文摘要:
      A signal de-noising method combining maximum between-cluster variance-empirical wavelet transform (MBCV-EWT) with singular value difference spectrum is proposed. It helps rolling bearing to overcome pattern aliasing and ensure the integrity and independence of each frequency component during vibration signal de-noising. First, in light of the uncertainty of traditional interval partitioning, an MBCV-EWT signal decomposition method is proposed. The signal spectrum is adaptively divided by the maximum inter-class variance, and a band-pass filter is constructed on each partition interval. Then, aiming at the redundancy of AM-FM component, impulse index is proposed to be the screening criteria of AM-FM and the best component is selected as a follow-up target. Finally, singular value decomposition is used for AM-FM. Signal de-noising is achieved according to the singular value difference spectrum. The simulation and experimental results show that the proposed method can achieve adaptive spectrum division. The problem of pattern aliasing can be effectively overcome, and the main components of the components obtained by decomposition are independent and complete. The amplitude modulation frequency components are accurately screened, and the effect of de-noising is obvious, so as to lay the foundation for fault recognition and prediction.
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