摘要
文章针对铁路通信系统故障诊断面临的挑战,提出了一种基于目标检测算法的故障诊断方法。文章首先分析了铁路通信系统的组成和常见故障类型,探讨了传统故障诊断方法的局限性。随后,研究了目标检测算法的基本原理和发展历程,重点介绍了YOLO系列算法。在此基础上,提出了一种改进的YOLOv5模型,并将其应用于铁路通信故障诊断,通过实验验证了该方法的有效性。研究结果表明,基于目标检测的故障诊断方法在准确性和效率方面均优于传统方法,为铁路通信系统的智能维护提供了新的解决方案。
This paper addresses the challenges of fault diagnosis in railway communication systems by proposing a fault diagnosis method based on object detection algorithms.The study begins by analyzing the composition of railway communication systems and common fault types,followed by an exploration of the limitations of traditional fault diagnosis methods.Subsequently,the fundamental principles and development of object detection algorithms are investigated,with a focus on the YOLO series algorithms.Building on this,an improved YOLOv5 model is proposed for railway communication fault diagnosis,and its effectiveness is validated through experiments.The results demonstrate that the object detection-based fault diagnosis method outperforms traditional methods in terms of accuracy and efficiency,offering a new solution for intelligent maintenance of railway communication systems.
作者
王英杰
杜宇
张童
Wang Yingjie;Du Yu;Zhang Tong(Hohhot Communication Section,China Railway Hohhot Bureau Group Co.,Ltd.Hohhot,Inner Mongolia Autonomous Region 010010,China)
出处
《新潮电子》
2025年第20期175-177,共3页
关键词
铁路通信
故障诊断
目标检测
YOLO算法
深度学习
railway communication
fault diagnosis
object detection
YOLO algorithm
deep learning
