基于卷积神经网络的高铁车轮损伤识别方法研究

Translated title of the contribution: Damage detection of wheels for high-speed rail based on Convolutional Neural Network

Qi'ang Wang, Teng Wang, Yiqing Ni

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

In this paper, the measured vibration data during the train operations were analyzed, a specific Convolutional Neural Network (CNN) structure for wheel damage detection was designed, network training was carried out based on vibration data with different labels for damage feature extraction, then the well trained CNN was used to realize long-term and efficient damage detection for high-speed rail wheel. Due to the black box characteristics of CNN, it is difficult to understand its mechanism for damage detection. In this paper, the convolution kernel of the CNN was visualized by using the gradient ascent method, and the response of the convolution kernel was analyzed by using the cross-power spectrum density to seek the physical explanation of feature extraction. Finally, the CNN damage detection results were compared with the results of Deep Neural Network and Recurrent Neural Network. It is concluded that CNN has a high accuracy for damage detection, with an average accuracy of 99.40%, and this accuracy is about 6% higher than other methods. In addition, the number of training parameters for CNN is smaller, so CNN is a light and efficient damage detection model for high-speed rail wheels.

Translated title of the contributionDamage detection of wheels for high-speed rail based on Convolutional Neural Network
Original languageChinese
Pages (from-to)781-787
Number of pages7
JournalZhongguo Kuangye Daxue Xuebao/Journal of China University of Mining and Technology
Volume49
Issue number4
Publication statusPublished - 1 Jul 2020

Keywords

  • Convolutional Neural Network
  • Cross-power spectrum density
  • Damage detection
  • High-speed rail wheel
  • Vibration response

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Geotechnical Engineering and Engineering Geology
  • Geology
  • Mechanical Engineering

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