地铁自动化系统故障预测优化研究  

Research on Fault Prediction and Optimization of Metro Automation System

作　　者：于数晓 YU Shuxiao

机构地区：[1]青岛地铁运营有限公司,山东青岛266000

出　　处：《今日自动化》2025年第2期133-135,共3页Automation Today

摘　　要：为了提高地铁自动化系统的运行可靠性与维护效率,研究基于深度学习的故障预测模型设计与优化方法,结合多源异构数据融合、在线学习技术及模型参数优化策略,构建了高效的预测与实时处理体系。研究结果表明,该技术显著提升了故障预测准确率与运维效率,降低了设备故障率与成本,具备良好的应用前景与推广价值。In order to improve the operational reliability and maintenance efficiency of subway automation systems,a deep learning based fault prediction model design and optimization method was studied.By combining multi-source heterogeneous data fusion,online learning technology,and model parameter optimization strategies,an efficient prediction and real-time processing system was constructed.The research results indicate that this technology significantly improves the accuracy of fault prediction and operational efficiency,reduces equipment failure rates and costs,and has good application prospects and promotion value.

关 键 词：地铁自动化系统 故障预测 深度学习 多源数据融合 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]