井下架空乘人装置自保护系统设计与研究
Design and Research of Self-Protection System for Underground Overhead Occupant Devices
范鹏飞1
作者信息
- 1. 晋能控股煤业集团马脊梁矿,山西大同 037000
- 折叠
摘要
矿井架空乘人装置温度预测是保证矿井架空乘人装置安全生产的有效途径,数字孪生技术是将现实世界的物理系统与虚拟世界的数字模型相结合的技术.为实现矿井架空乘人装置电机温度的在线监测和超前感知,提出了基于数字孪生和循环神经网络的温度预测和超前性感知方法.结果表明,数字孪生系统能有效监测矿井架空乘人装置运行过程中电机的实时温度状态,数字孪生和循环神经网络的集成可准确预测和主动检测矿井架空乘人装置电机的温度变化.
Abstract
The temperature prediction of mine overhead passenger devices is an effective way to ensure the safe production of mine overhead passenger devices.Digital twin technology is a technology that combines physical systems in the real world with digital models in the virtual world.A temperature prediction and advanced sensing method based on digital twin and recurrent neural network is proposed to achieve online monitoring and advanced sensing of the motor temperature of overhead passenger devices in mines.The results show that the digital twin system can effectively monitor the real-time temperature status of the motor during the operation of the mine overhead passenger device.The integration of digital twin and recurrent neural network can accurately predict and actively detect the temperature changes of the motor in the mine overhead passenger device.
关键词
架空乘人装置/数字孪生/循环神经网络/状态预测Key words
orerhead passenger device/digital twin/recurrent neural network/state prediction引用本文复制引用
出版年
2024
NETL
NSTL
万方数据