Volume 50 Issue 5
May  2024
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Article Navigation >  Journal of Mine Automation  > 2024  >  50(5): 151-156
XU Jun, ZHAO Xiaohu, HOU Nianqi, et al. A maintenance guidance system for coal mine electromechanical equipment based on improved YOLOv5s[J]. Journal of Mine Automation，2024，50（5）：151-156.  doi: 10.13272/j.issn.1671-251x.2023090069
Citation: XU Jun, ZHAO Xiaohu, HOU Nianqi, et al. A maintenance guidance system for coal mine electromechanical equipment based on improved YOLOv5s[J]. Journal of Mine Automation，2024，50（5）：151-156.  doi: 10.13272/j.issn.1671-251x.2023090069
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A maintenance guidance system for coal mine electromechanical equipment based on improved YOLOv5s

doi: 10.13272/j.issn.1671-251x.2023090069
  • 1.

    Information Center, Xuzhou University of Technology, Xuzhou 221000, China

  • 2.

    The National and Local Joint Engineering Laboratory of Internet Application Technology on Mine, China University of Mining and Technology, Xuzhou 221008, China

  • 3.

    School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221008, China

  • Received Date: 2023-09-22
  • Rev Recd Date: 2024-05-22
    • Available Online: 2024-06-13
    Abstract
  • In order to solve the problems of large workload and low versatility of QR code labelling and complex implementation and difficult deployment of existing no-registration recognition methods in the auxiliary maintenance of coal mine electromechanical equipments, a coal mine electromechanical equipments maintenance guidance system based on improved YOLOv5s is proposed. The system consists of a equipment no-registration recognition module, a fault maintenance guidance module, and a remote expert access guidance module. The equipment no-registration recognition module collects images of faulty equipments through the camera on HoloLens glasses, and analyzes and processes them through an improved YOLOv5s image recognition algorithm to recognize the faulty equipment model. The fault maintenance guidance module automatically matches and calls the preset mixed reality disassembly and assembly model based on the model of the faulty equipment, forming a maintenance guidance solution. The remote expert access guidance module achieves interaction between remote experts and on-site maintenance personnel through audio and video sessions, virtual annotation, and other methods. In order to ensure an immersive experience for users when using mixed reality equipment, ShuffleNetV2 is used to replace the Backbone in YOLOv5s to obtain the YOLOv5s-SN2 network, which reduces the number of model parameters and computational overhead. The experimental results show that YOLOv5s-SN2 has a slight decrease in precision compared to YOLOv5s, but the number of floating-point operations per second (FLOPS) has decreased from 16.5×109 to 7.6×109, and the number of parameters has decreased from 15.6×106 to 8.2×106. Among the YOLO series models, YOLOv5s-SN2 has the best performance. Taking the three leaf Roots blower as an example to verify the overall effectiveness of the system, the results show that YOLOv5s-SN2 can quickly recognize the motor model, call the matching virtual model and maintenance process. The remote experts can assist on-site personnel in electromechanical equipment maintenance through methods such as audio and video access and annotation.

     

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