摘要
针对当前掘进机电设备故障率高、故障诊断时间长的现状,对其进行了详细的总结,并提出了基于传感器网络的实时监测与机器学习的故障诊断方法,建立了结合传感器网络、机器学习和专家系统等技术的诊断系统,为提高掘进机电设备的可靠性和缩短故障排除时间奠定了基础。
The current situation of high failure rate and long fault diagnosis time of electromechanical excavating equipment was summarized in detail,and a fault diagnosis method based on real-time monitoring and machine learning based on sensor network was proposed,and a diagnosis system combining sensor network,machine learning and expert system was established,laying a foundation for improving the reliability of the excavating electromechanical equipment and shortening the troubleshooting time.
作者
赵思远
ZHAO Siyuan(No.2 Coal Mine,Huayang New Material Technology Group Co.,Ltd.,Yangquan 045000,Shanxi,China)
出处
《能源与节能》
2024年第9期122-124,203,共4页
Energy and Energy Conservation
关键词
掘进机
机电设备
故障诊断
系统维护
excavating machine
electromechanical equipment
fault diagnosis
system maintenance
