摘要
矿山机电设备是矿业采掘、生产中的重要设备,其工作状况对矿山企业的生产效率和安全起着重要作用。文中综合分析矿山机电设备常见故障类型,探索高效精确的故障预测和诊断技术,研究将传统信号处理技术、机器学习算法和物联网技术相融合,构建多维度、多层次的故障诊断系统。研究成果有效提升矿山设备故障诊断的精度,降低非计划停产次数,旨在为矿山智能维护管理提供技术支撑。
Mining electromechanical equipment is an important equipment in mining and production,and its working condition plays an important role in the production efficiency and safety of mining enterprises.The article comprehensively analyzes the common types of faults in mining electromechanical equipment,explores efficient and accurate fault prediction and diagnosis techniques,and studies the integration of traditional signal processing technology,machine learning algorithms,and Internet of Things technology to construct a multi-dimensional and multi-level fault diagnosis system.The research results effectively improve the accuracy of fault diagnosis for mining equipment,reduce unplanned shutdowns,and aim to provide technical support for intelligent maintenance and management of mines.
作者
张斌斌
Zhang Binbin(Third Mine Construction Department of Pingmei Shenma Jianggong Group,Pingdingshan,Henan,China,467002)
出处
《仪器仪表用户》
2025年第2期120-122,共3页
Instrumentation
关键词
矿山机电
设备故障
诊断技术
mine electromechanical
equipment fault
diagnosis technology
