摘要
大型旋转设备作为能源、化工等工业领域的核心装备,其安全稳定运行至关重要。传统监测系统存在报警滞后、误报率高、依赖专家经验等问题,难以实现有效的安全预控。本研究旨在构建一个集设备状态可视化监控、多源数据融合与智能分析于一体的智能安全预控系统,重点研究了系统的总体架构,并深入探讨了其核心的智能预警异常模型。该模型采用CNN-LSTM混合深度学习算法,能够有效捕捉设备状态的时空特征,实现早期异常检测,同时,引入动态预警阈值与多级风险评估机制,提升了预警的准确性和前瞻性。通过案例模拟验证,该系统能够显著提前预警时间,降低误报率,为实现从“被动响应”到“主动预警”的设备安全管理模式转变提供了有效的技术解决方案。
As the core equipment in industrial fields such as energy and chemical engineering,the safe and stable operation of large rotating equipment is crucial.Traditional monitoring systems suffer from issues such as alarm lag,high false alarm rates,and reliance on expert experience,making it difficult to achieve effective safety pre-control.This study aims to construct an intelligent security pre-control system that integrates device status visualization monitoring,multi-source data fusion,and intelligent analysis.The study focuses on the overall architecture of the system and explores in depth its core intelligent early warning anomaly model,which uses a CNN-LSTM hybrid deep learning algorithm to effectively capture the spatiotemporal characteristics of device status and achieve early anomaly detection.At the same time,dynamic warning thresholds and multi-level risk assessment mechanisms have been introduced to improve the accuracy and foresight of warnings.Through case simulation verification,the system can significantly advance the warning time,reduce the false alarm rate,and provide an effective technical solution for the transformation of equipment safety management mode from"passive response"to"active warning".
作者
刘昊
李明君
金喆
甄宇轩
LIU Hao;LI Mingjun;JIN Zhe;ZHEN Yuxuan(National Energy Boxing Power Generation Co.,Ltd.,Binzhou 256507,China)
出处
《电工材料》
2025年第6期91-94,共4页
Electrical Engineering Materials
关键词
大型旋转设备
智能安全预控系统
可视化监控
智能预警
异常检测
large-scale rotating equipment
intelligent safety pre-control system
visual monitoring
intelligent warning
anomaly detection
