JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2017, Vol. 47 ›› Issue (3): 89-95.doi: 10.6040/j.issn.1672-3961.0.2016.270

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Feature extraction method of rolling bearing inner ring in wind turbine based on improved EMD and feature box

YU Qingmin1, LI Xiaolei1*, ZHAI Yong2   

  1. 1. School of Control Science and Engineering, Shandong University, Jinan 250061, Shandong, China;
    2. School of Mechanical Engineering, Northeast Dianli University, Jilin 132012, Jilin, China
  • Received:2016-07-18 Online:2017-06-20 Published:2016-07-18

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Abstract: According to the characteristics of vibration signal of rolling bearing inner ring in direct-driven wind turbine, a new method of fault diagnosis by improved empirical mode decomposition(EMD)and feature box was put forward. The original signal was decomposed by improved EMD to get a finite number of stationary intrinsic mode functions(IMFs). The characteristics of amplitude domain parameters such as mean and variance were extracted, which were turned into feature matrix chose by effectiveness. To perform data smoothing processing, The feature matrix was divided into boxes and replaced by means of data in each box. Examples showed that the feature matrix, which was divided into boxes finally, could effectively extract the fault feature of rolling bearing, and reduce the over fitting of the machine learning model.

Key words: data sub box, bearing inner ring, feature extraction, fault diagnosis, improved EMD, cost sensitive

CLC Number: 

  • TH17
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