提升机天轮运行状态监控及故障检测技术
Hoist Sheave Running State Monitoring and Fault Detection Technology
尹伟1
作者信息
摘要
为解决矿井提升机系统故障检出率低、运行稳定性差的问题,在对提升机结构和常见故障机理进行分析的基础上,提出了一种新的提升机天轮运行状态监控和故障检测技术,对提升机天轮运行时的振动、偏摆、温度信息实时采集和分析,同时提出了多尺度卷积神经网络的天轮轴承故障判断方法.实际应用表明,该运行状态监控和故障检测技术能够实现对提升机故障的精确监控,准确率达到了98.4%,能够将提升机的运行故障率降低77.6%,对提高提升机的运行稳定性和安全性具有十分重要的意义.
Abstract
In order to solve the problems of low fault detection rate and poor operation stability of mine hoist system,based on the analysis of hoist structure and common fault mechanism,a new operation state monitoring and fault detection technology of hoist sheave is proposed.The technology can realize the real-time acquisition and analysis of the vibration,yaw and temperature information of the hoist sheave during operation.At the same time,a fault judgment method of the sheave bearing based on multi-scale convolu-tional neural network is proposed.The practical application shows that the operation state monitoring and fault detection technology can realize the accurate monitoring of the hoist fault,the accuracy rate reaches 98.4%,and the operation failure rate of the hoist can be reduced by 77.6%,which is of great significance to improve the operation stability and safety of the hoist.
关键词
提升机/状态监控/故障检测/卷积神经网络Key words
hoist/state monitoring/fault detection/convolutional neural network引用本文复制引用
出版年
2024
NETL
NSTL
万方数据