基于机器学习的道岔故障特征提取及诊断方法探析  

Analysis of Turnout Fault Feature Extraction and Diagnosis Method Based on Machine Learning

作　　者：虞梦月 Yu Mengyue

机构地区：[1]西安铁路职业技术学院,陕西西安710026

出　　处：《时代汽车》2024年第17期175-177,共3页Auto Time

基　　金：西安铁路职业技术学院院级课题“行车安全驱动的道岔故障特征提取与诊断方法”(XTZY23K14)

摘　　要：道岔设备的可靠运行对铁路运输安全至关重要,随着人工智能技术的快速发展,其在铁路系统中的应用日益增多,特别是在道岔故障诊断领域展现出巨大的潜力。文章主要介绍了基于机器学习的故障诊断过程,深入分析了基于神经网络和支持向量机的故障诊断方法。通过对历史数据和故障模式的分析,这些方法能够实现监测道岔故障、判断故障类型的目的,显著提高了道岔故障诊断的准确性和效率,为铁路安全、可靠和高效运行提供了强有力的技术支撑。The reliable operation of turnout equipment is very important for the safety of railway transportation,and with the rapid development of artificial intelligence technology,its application in railway systems is increasing,especially in the field of turnout fault diagnosis.This paper mainly introduces the fault diagnosis process based on machine learning,and deeply analyzes the fault diagnosis method based on neural network and support vector machine.Through the analysis of historical data and failure modes,these methods can achieve the purpose of monitoring turnout faults and judging fault types,significantly improve the accuracy and efficiency of turnout fault diagnosis,and provide strong technical support for the safe,reliable and efficient operation of railways.

关 键 词：道岔 机器学习 特征提取 故障诊断 

分 类 号：U21[交通运输工程—道路与铁道工程]