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  • ISSN：0577-6686
机械工程学报

机械工程学报 ›› 2017, Vol. 53 ›› Issue (3): 73-80.doi: 10.3901/JME.2017.03.073

• 机械动力学 • 上一篇    下一篇

基于瞬时包络尺度谱熵的滚动轴承早期故障奇异点识别及特征提取*

孙鲜明1, 刘欢2, 赵新光3, 周勃4   

  1. 1. 沈阳化工大学机械工程学院 沈阳 110142；
    2. 沈阳化工大学信息工程学院 沈阳 110142；
    3. 辽宁科技学院机械工程学院 本溪 117004；
    4. 沈阳工业大学建筑工程学院 沈阳 110870
  • 出版日期:2017-02-05 发布日期:2017-02-05
  • 作者简介:

    作者简介：孙鲜明，男，1987年出生，博士。主要研究方向为机械动力学及故障诊断。

    E-mail：fisher101990@163.com

  • 基金资助:
    * 国家自然科学基金(51575361)和辽宁省教育厅一般项目(L2015277，; L2015427)资助项目; 20160318收到初稿，20160621收到修改稿;

Singular Point Recognition and Feature Extraction for Incipient Bearing Fault Based on Instantaneous Envelope Scalogram Entropy

SUN Xianming1, LIU Huan2, ZHAOXingung3, ZHOU Bo4   

  1. 1. School of Mechanical Engineering， Shenyang University of Chemical Technology， Shenyang 110142;
    2. School of Information Engineering， Shenyang University of Chemical Technology， Shenyang 110142;
    3. School of Mechanical Engineering， Liaoning Institute of Science and Technology， Benxi 117004;
    4. School of Architecture and Civil Engineering， Shenyang University of Technology， Shenyang 110870
  • Online:2017-02-05 Published:2017-02-05

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摘要：

滚动轴承故障信号具有较强的非平稳特性，并且极易受齿轮等噪源污染，故障特征信息微弱，特别当滚动轴承处于故障早期，上述问题尤为严重。针对这一难点，提出基于瞬时包络尺度谱熵的滚动轴承早期故障奇异点识别及特征提取方法。应用重分配尺度谱对轴承的包络信号进行时频分解，计算每一时刻的功率谱熵，以获取信号的瞬时包络尺度谱熵(Instantaneous envelope scalogram entropy，IESE)，则信号IESE曲线发生畸变的位置，即是轴承故障表征最为明显的时刻，进而可以提取轴承故障信号的最优故障表征时段(Optimal fault characterization phase，OFCP)，应用包络解调和包络尺度谱分析OFCP，以提取轴承故障特征频率。实测信号分析结果表明，该方法能有效提取轴承故障早期的微弱故障特征信息。

关键词: 包络尺度谱, 瞬时包络尺度谱熵, 早期故障, 最优故障表征时段, 滚动轴承

Abstract:

The non-stationary characteristics of the bearing fault signal is strong, and the signal is easy to be contaminated by the gear and other noise sources, the fault feature information is weak, especially when the rolling bearing is at the early fault stage , the above problem is particularly serious. To solve the problem above, a method of singular point recognition and feature extraction for the incipient bearing fault is presented based on instantaneous envelope scalogram entropy(IESE). The envelope signal is decomposed by reassigned wavelet scalogram, and the power spectrum entropy of each moment is calculated to obtain the instantaneous envelope scalogram entropy of the signal. The distortion position of the IESE curve of the signal is the most obvious time of bearing fault characterization. And then the optimal fault characterization phase(OFCP) of bearing fault signal could be extracted, and the OFCP could be used to extract the characteristic frequency of bearing fault. The results showed that the method could effectively extract the weak fault feature information of the signal of early fault bearing.

Key words: envelope scalogram, incipient fault, instantaneous envelope scalogram entropy, optimal fault characterization phase, rolling bearings

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