摘要
在现代钢铁制造过程中,线棒材轧机架的振动监控是保障生产安全和提高生产效率的关键技术之一。由于轧机在生产线上的关键作用,其产生的振动若无法得到有效监控和预警,可能导致设备损坏甚至生产事故。长短期记忆网络(LSTM)是一种特别设计来处理和预测时间序列数据问题的深度学习模型,能够有效地利用历史数据中的长期依赖信息,对未来的状态进行预测。因此结合LSTM处理振动数据成为一种潜在的解决方案,以实现动态和实时的预警系统。
In the modern steel manufacturing process,vibration monitoring of wire rod rolling mill stands is one of the key technologies to ensure production safety and improve production efficiency.Due to the crucial role of rolling mills in the production line,if the vibrations generated by them cannot be effectively monitored and predicted,it may lead to equipment damage or even production accidents.Long Short-Term Memory(LSTM)is a deep learning model specifically designed to handle and predict time-series data problems,which can effectively utilize long-term dependencies in historical data to predict future states.Therefore,the combination of LSTM for processing vibration data presents a potential solution for implementing a dynamic and real-time warning system.
作者
唐桢
李绍仁
Tang Zhen;Li Shaoren(Xinyu Iron and Steel Group Co.,Ltd.,Xinyu,Jiangxi,China,338000)
出处
《仪器仪表用户》
2024年第12期4-6,共3页
Instrumentation
关键词
长短期记忆网络
线棒材轧机架
振动动态预警系统
long short-term memory network
wire rod rolling mill stands
vibration dynamic warning system
