摘要
随着工业生产的自动化和智能化程度不断提高,确保生产设备的稳定运行对于提高生产效率和产品质量至关重要。文中聚焦于生产设备异常运行在线检测方法,该方法考虑了离线数据的重要性,通过数据融合技术,融合离线历史数据和实时在线监测数据,同时消除冗余数据,分析得到数据特征,并据此建立不同数据间的关联。对多源历史数据融合过程中的外在扰动与内在不相关扰动数据进行消除,实现对生产设备异常运行状态的准确及时检测。与传统自适应优化模型进行的对比实验结果表明,所提方法可实现生产设备异常运行在线检测,且鲁棒性更强,可显著提升生产设备的异常检测能力。
With the continuous improvement of automation and intelligence in industrial production,ensuring the stable operation of production equipment is crucial for improving production efficiency and product quality.This study focuses on the online detection method for abnormal operation of production equipment,which considers the importance of offline data.Through data fusion technology,it integrates offline historical data and real-time online monitoring data,eliminates redundant data,analyzes data features,and establishes correlations between different data based on this.Compensate for external and internal irrelevant disturbances in the fusion process of multi-source historical data,and achieve accurate and timely detection of abnormal operating states of production equipment.The comparative experimental results with traditional adaptive optimization models show that the proposed method can achieve online detection of abnormal operation of production equipment,and has stronger robustness,which can greatly improve the ability of abnormal detection of production equipment.
作者
农振昌
席强
刘丰
王凤祥
赵红标
NONG Zhenchang;XI Qiang;LIU Feng;WANG Fengxiang;ZHAO Hongbiao(Longtan Hydroelectric Power Plant,Longtan Hydropower Development Co.,Ltd.,Nanning 530000,China)
出处
《电子设计工程》
2025年第20期76-79,84,共5页
Electronic Design Engineering
基金
2022年龙滩水电开发有限公司龙滩水力发电厂信息化项目(CDT-LTHPC-E-2395)。
关键词
离线数据
生产设备
异常运行
在线检测
offline data
production equipment
abnormal operation
online detection
