摘要
文章针对煤矿掘进设备管理中存在的自动化程度低等问题,提出了一种基于深度学习技术的智能管控系统。该系统设计了一种用于设备状态预测的深度学习模型,实现对掘进机关键部件的性能衰退预测;同时,设计了一个自适应模糊预测控制器,根据预测结果动态调整控制参数。实验结果表明,该系统有效提升了设备故障预测的准确率和生产效率。
Aiming at the problem of low degree of automation in the management of coal mine tunneling equipment,this paper proposes an intelligent control system based on deep learning technology.The system designs a deep learning model for equipment state prediction to realize the performance degradation prediction of key components of roadheader;at the same time,an adaptive fuzzy prediction controller is designed to dynamically adjust the control parameters according to the prediction results.The experimental results show that the system effectively improves the accuracy and production effi ciency of equipment fault prediction.
作者
张碧显
ZHANG Bixian(Lu'an Chemical Group Gucheng Coal Mine,Changzhi 046000,Shanxi,China)
出处
《凿岩机械气动工具》
2025年第2期145-147,共3页
Rock Drilling Machinery & Pneumatic Tools
关键词
深度学习
智能管控
状态预测
设备故障
deep learning
intelligent control
state prediction
equipment fault
