Volume 32 Issue 5
Sep.  2023
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ZHANG Changfan, HUANG Congcong, HE Jing, “Defects Recognition of Train Wheelset Tread Based on Improved Spiking Neural Network,” Chinese Journal of Electronics, vol. 32, no. 5, pp. 941-954, 2023, doi: 10.23919/cje.2022.00.162
Citation: ZHANG Changfan, HUANG Congcong, HE Jing, “Defects Recognition of Train Wheelset Tread Based on Improved Spiking Neural Network,” Chinese Journal of Electronics, vol. 32, no. 5, pp. 941-954, 2023, doi: 10.23919/cje.2022.00.162

Defects Recognition of Train Wheelset Tread Based on Improved Spiking Neural Network

doi: 10.23919/cje.2022.00.162
Funds:  This work was supported by the National Natural Science Foundation of China (52172403, 62173137), the Hunan Provincial Natural Science Foundation of China (2021JJ50001), and the Project of Hunan Provincial Department of Education (19A137, 18A267)
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  • Author Bio:

    Changfan ZHANG received the M.S. degree in Southwest Jiaotong University in 1989 and Ph.D. degree from Hunan University in 2001, respectively. Now he is a Professor at Hunan University of Technology. His main research interests include nonlinear control and application. (Email: zcf@hut.edu.cn)

    Congcong HUANG received the B.E. degree in Hunan University of Technology in 2019. Now she is an M.S. candidate at Hunan University of Technology. Her main research interests include Intelligent detection and control. (Email: 1393117258@qq.com)

    Jing HE (corresponding author) received the M.S. degree in Central South University of Forestry and Technology in 2002 and Ph.D. degree from National University of Defense Technology in 2009, respectively. Now she is a Professor at Hunan University of Technology. Her main research interests include electro-mechanical system fault diagnosis.(Email: hejing@hut.edu.cn)

  • Received Date: 2022-06-06
  • Accepted Date: 2022-08-10
  • Available Online: 2022-10-20
  • Publish Date: 2023-09-05
  • Surface defect recognition of train wheelset is crucial for the safe operation of the train wheel system. However, due to the diversity and complexity of such defects, it is difficult for existing algorithms to make rapid and accurate recognitions. To solve this problem, an improved spiking neural network (SNN) based defect recognition method for train wheelset tread is proposed. Specifically, a hybrid convolutional encoding module is first designed to conduct image-to-spike conversion and to create multi-scale sparse representations of the features. Second, a residual spiking convolutional neural network is implemented to extract spiking features optimally, and a multi-scale structure is adopted to enhance the SNN’s ability to handle details. A channel attention module is then incorporated to re-calibrate the weights of four-dimensional spiking feature maps. Finally, effective spiking features are obtained according to the recognition decisions which are made. The experimental results showed that the proposed method improved the accuracy of defect recognition. The recognition time of a single image is only 0.0195 s on average. The overall performance of the proposed method is noticeably superior to current mainstream algorithms.
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