Volume 21 Issue 6
Dec.  2021
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2021  >  21(6): 247-258
ZHANG Long, ZHEN Can-zhuang, XIONG Guo-liang, WANG Chao-bing, XU Tian-peng, TU Wen-bing. Locomotive bearing fault diagnosis based on deep time-frequency features[J]. Journal of Traffic and Transportation Engineering, 2021, 21(6): 247-258. doi: 10.19818/j.cnki.1671-1637.2021.06.019
Citation: ZHANG Long, ZHEN Can-zhuang, XIONG Guo-liang, WANG Chao-bing, XU Tian-peng, TU Wen-bing. Locomotive bearing fault diagnosis based on deep time-frequency features[J]. Journal of Traffic and Transportation Engineering, 2021, 21(6): 247-258. doi: 10.19818/j.cnki.1671-1637.2021.06.019
shu

Locomotive bearing fault diagnosis based on deep time-frequency features

doi: 10.19818/j.cnki.1671-1637.2021.06.019
  • 1.

    School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, Jiangxi, China

  • 2.

    CRRC Qishuyan Co., Ltd., Changzhou 213011, Jiangsu, China

Funds:

National Natural Science Foundation of China 51665013

Postgraduate Innovation Fund Project of Jiangxi Province YC2019-S243

Science and Technology Research Project of Jiangxi Education Department 191327

More Information
  • Author Bio:

    ZHANG Long(1980-), male, associate professor, PhD, longzh@126.com

  • Corresponding author: ZHEN Can-zhuang(1994-), male, master, 526991152@qq.com
  • Received Date: 2021-06-20
    Available Online: 2022-02-11
  • Publish Date: 2021-12-01
    Abstract
  • To address the problems such as the unsatisfactory fault feature extraction and low diagnostic accuracy of existing locomotive bearing diagnosis methods, a new method for diagnosing locomotive bearing faults was developed based on the deep time-frequency features. Dual-channel one-dimensional and two-dimensional convolutional neural networks (CNNs) were separately adopted to extract the deep features from the input one-dimensional original and two-dimensional time-frequency signals extracted by the continuous wavelet transform (CWT). A one-dimensional CNN was employed for the upper channel such that the input one-dimensional original signals could effectively reflect the global characteristics of the signals in the time domain. A two-dimensional CNN was applied for the lower channel such that the input two-dimensional time-frequency domain signals could reflect the subtle local changes in the signals from multiple angles. The upper- and lower-channel features were automatically fused in the fusion layer into a new deep time-frequency feature. Then, the extracted deep fusion time-frequency features were classified and identified by a normalized exponential function. Finally, seven types of locomotive bearing data measured in a locomotive depot were analyzed to verify the practical engineering application value of this method. Research results indicate that the average diagnosis accuracies of the proposed method for the seven types of locomotive bearing faults are as high as 100%. Compared with the one-dimensional CNN model, two-dimensional CNN model, and support vector machine (SVM) model, the average diagnosis accuracy of the proposed model increases by 0.7%, 1.9%, and 2.2%, respectively. The distribution intervals of each fault type in the deep time-frequency features are regular and orderly, and the intra-class spacing is very small. Conversely, the features extracted by the single one-dimensional and two-dimensional CNN models exhibit irregular distribution intervals for all fault types, and the intra-class spacing is large. This verifies the superiority of the proposed model in extracting deep features. Therefore, it is an effective model to address the issues in the locomotive bearing fault diagnosis. 4 tabs, 17 figs, 30 refs.

     

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