摘要
介绍了煤矿生产的重要性和机电故障会对其产生的影响,探讨了传统和现代故障诊断技术,特别关注的是基于传感器数据和机器学习的方法。详细阐述了电机过热、传动链条断裂等常见的故障案例,在此基础上,借助机器学习技术,提出了针对这些故障的诊断方法,包括特征工程、模型构建和训练等。最后,探讨了预防性维护和智能化维护策略,并对未来故障诊断与维护技术的发展进行了展望。研究旨在提高矿井生产的安全性和效率,为确保煤矿井下机电设备的可靠运行提供有力支持。
This paper introduced the importance of coal mining production and the impact of electromechanical faults on it,explored traditional and modern fault diagnosis techniques,with a particular focus on methods based on sensor data and machine learning.The case analysis elaborated on common fault cases such as motor overheating and transmission chain breakage.On this basis,with the help of machine learning technology,diagnostic methods for these faults were proposed,including feature engineering,model construction,and training.Finally,preventive maintenance and intelligent maintenance strategies were discussed,and the future development of fault diagnosis and maintenance technologies was discussed.The aim was to improve the safety and efficiency of mine production,and provided strong support for ensuring the reliable operation of underground electromechanical equipment in coal mines.
作者
郭文强
GUO Wenqiang(No.1 Coal Mine,Shanxi Huayang Group New Energy Co.,Ltd.,Yangquan 045000,Shanxi,China)
出处
《能源与节能》
2024年第8期204-206,共3页
Energy and Energy Conservation
关键词
煤矿
井下掘进机
机电故障维护
coal mine
underground tunneling machine
electromechanical fault maintenance
