• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 27 Issue 1
Jan.  2014
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Article Contents
QIN Na, JIN Weidong, HUANG Jin, LI Zhimin, LIU Jingbo. Feature Extraction of High Speed Train Bogie Based on Ensemble Empirical Mode Decomposition and Sample Entropy[J]. Journal of Southwest Jiaotong University, 2014, 27(1): 27-32. doi: 10.3969/j.issn.0258-2724.2014.01.005
Citation: QIN Na, JIN Weidong, HUANG Jin, LI Zhimin, LIU Jingbo. Feature Extraction of High Speed Train Bogie Based on Ensemble Empirical Mode Decomposition and Sample Entropy[J]. Journal of Southwest Jiaotong University, 2014, 27(1): 27-32. doi: 10.3969/j.issn.0258-2724.2014.01.005

Feature Extraction of High Speed Train Bogie Based on Ensemble Empirical Mode Decomposition and Sample Entropy

doi: 10.3969/j.issn.0258-2724.2014.01.005
  • Received Date: 15 Sep 2012
  • Publish Date: 25 Jan 2014
  • To monitor the working condition of key components of high speed train bogie in time, a novel method for feature extraction is proposed by combination of ensemble empirical mode decomposition (EEMD) and sample entropy theory. Vibration signals are obtained from train body and bogie under four typical working conditions, such as normal condition, air spring fault, lateral damper fault, and yaw damper fault. After EEMD, signals have been decomposed into a series of intrinsic mode functions (IMFs), and the sample entropies of these IMFs constitute a high dimensional characteristic vector. Finally, the support vector machine is used to identify the fault conditions based on the characteristic vector. The experimental result shows that the recognition rate is 88% at the speed of 200 km/h. Therefore, this feature extraction method is effective for high speed train bogie fault signals.

     

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