测试技术学报2024，Vol.38Issue(4)：435-440,6.DOI:10.3969/j.issn.1671-7449.2024056
基于DNN-SVM的无线专网设备故障识别与定位系统研究
Research on Fault Detection and Localization System for Wireless Private Network Devices Based on DNN-SVM
摘要
Abstract
A solution based on DNN-SVM is proposed for fault detection and localization in the electric power wireless mesh network system.The timely and accurate identification and localization of faults in the electric power wireless mesh network system pose challenges for maintenance and repair work.In this paper,real-time data from the electric power wireless mesh network system,including signal strength,signal quality,and PCE operating status,is collected using dedicated devices.A DNN-SVM algorithm is constructed to achieve simultaneous fault detection and localization in the wireless mesh network.The DNN is used to discriminate fault states,while the multilayer binary SVM is employed for fault-type clas-sification.Experimental validation is conducted on an actual electric power wireless mesh network data-set.The decision time for a single data sample is in the millisecond range,and the overall average accu-racy rate is 80%.关键词
无线专网/深度学习/支持向量机/故障检测Key words
wireless private network/deep learning/SVM/fault detection分类
信息技术与安全科学引用本文复制引用
蒋跃宇,夏凌,蒋冰越,韩伟,王康..基于DNN-SVM的无线专网设备故障识别与定位系统研究[J].测试技术学报,2024,38(4):435-440,6.基金项目
国网江苏省电力有限公司孵化项目(JF2022026) （JF2022026）
