• CN：11-2187/TH
• ISSN：0577-6686

• 机械动力学 •

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*

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

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.