摘要
基于红外热成像和机器学习的动态转向架监测系统可以对货运列车的转向架进行动态、可视化监控分析。系统利用了红外热成像技术和机器学习图像识别技术,全方位对转向架的关键部件故障,如热轴故障、抱闸故障、制动缓解不良和不制动等故障进行全面监测诊断和实时预报。系统实现了地面设备对货运列车车辆转向架部位的关键部件动态监测,提高了货运列车安全运行能力。
The dynamic bogie monitoring system based on infrared thermal imaging and machine learning can perform dynamic and visual monitoring and analysis on the bogies of freight trains.The system utilizes infrared thermal imaging technology and machine learning image recognition technology,which can comprehensively detect the common hot axle failures,brake failures,poor braking relief,and non-braking of the bearing devices,wheel sets and basic braking components of the bogie faults are fully monitored and diagnosed and real-time forecasted.The system realizes the dynamic monitoring of the bogie parts of freight trains by ground equipment which greatly improves the operational safety and prevention capabilities of freight trains.
作者
杨二斌
王文刚
问国辉
YANG Erbin;WANG Wengang;WEN Guohui(China Energy Railway Equipment Co.,Ltd.,Beijing 100011,China;Beijing TieKeHeLi Technology Co.,Ltd.,Beijing 100082,China)
出处
《铁道机车车辆》
北大核心
2022年第4期52-58,共7页
Railway Locomotive & Car
关键词
红外热成像
机器学习
图像融合
动态故障预测
铁路货车
转向架
infrared thermal imaging
machine learning
image fusion
dynamic fault prediction
freight trains
bogie
