多元信号异常检测综述  

Overview of Multivariate Signal Anomaly Detection

作　　者：戈宁振 赵正喜 闫子熙 GE Ningzhen;ZHAO Zhengxi;YAN Zixi(Hebei Institute of Radio and Television Science and Technology,Shijiazhuang,050011,China)

机构地区：[1]河北省广播电视科学技术研究所,石家庄050011

出　　处：《网络新媒体技术》2025年第2期1-11,共11页Network New Media Technology

摘　　要：随着传感器及物联网技术的快速发展,多元信号异常检测在多个领域的研究与应用取得了巨大进展。本文主要从基于预测、基于重构和基于混合模型3个角度,对近年来深度学习算法在多元信号异常检测领域的应用进行归纳总结,为研究者了解多元信号异常检测的研究动态和发展趋势提供参考。With the rapid development of sensor and Internet of Things(IoT)technologies,the research and application of multivariate signal anomaly detection have made great progress in many fields.In this paper,we summarize the applications of deep learning algorithms in multivariate signal anomaly detection in recent years from the perspectives of prediction-based,reconstruction-based,and hybrid model-based,to provide researchers with a reference for understanding the research dynamics and development trends of multivariate signal anomaly detection.

关 键 词：多元信号 异常检测 深度学习 重构网络 混合模型 

分 类 号：TN911.7[电子电信—通信与信息系统] TP391[电子电信—信息与通信工程]