摘要
本文提出了基于深度学习的矿山机电设备智能故障预测方法。首先,收集并预处理历史数据,构建训练集;然后,设计训练深度神经网络模型,用于学习设备运行状态与故障关系;最后,在预测阶段,输入实时数据到训练好的模型实现故障预测。实验结果表明,该方法能有效预测设备故障,提供维护决策支持,减少停机时间,提高设备利用率,具有更高预测精度和适应性。
This paper proposes an intelligent fault prediction method for mining electrical and mechanical equipment based on deep learning.Firstly,historical data is collected and preprocessed to construct a training set.Then,a deep neural network model is designed and trained to learn the relationship between equipment operating conditions and faults.Finally,in the prediction stage,realtime data is input into the trained model to achieve fault prediction.The experimental results show that this method can effectively predict equipment faults,provide maintenance decision support,reduce downtime,improve equipment utilization,and achieve higher prediction accuracy and adaptability.
作者
幸伟鹏
XING Weipeng(Shanxi Coking Coal Xishan Coal Power Inclined Ditch Coal Preparation Plant,Lyuliang 033602,China)
出处
《中国矿业》
北大核心
2024年第S01期238-242,共5页
China Mining Magazine
基金
国家自然科学基金项目“基于数据挖掘的煤矿安全可视化管理模型及图元体系研究”资助(编号:61471362)
关键词
矿山机电设备
故障预测
深度学习
智能维护
神经网络
mining electrical and mechanical equipment
fault prediction
deep learning
intelligent maintenance
neural network
