基于深度学习的继电器寿命预测方法研究综述  

Review of Relay Life Prediction Methods Based on Deep Learning

作　　者：刘百鑫 王召斌[1] 乔青云 朱佳淼 李朕 LIU Baixin;WANG Zhaobin;QIAO Qingyun;ZHU Jiamiao;LI Zhen(School of Electronics and Information,Jiangsu University of Science and Technology,Zhenjiang 212003,China)

机构地区：[1]江苏科技大学电子信息学院,江苏镇江212003

出　　处：《电器与能效管理技术》2021年第12期1-6,33,共7页Electrical & Energy Management Technology

基　　金：国家自然科学基金项目(51507074);国防基础科研计划稳定支持专题项目(JCKYS2020604SSJS010)。

摘　　要：近年来,随着科技的不断发展,继电器等电器元器件的剩余使用寿命逐渐成为了研究的热点,准确、高效地分析海量的监测数据是故障检测与健康管理的主要任务。由于传统方法受限于依靠物理模型与先验知识,深度学习方法应运而生。首先介绍了继电器剩余使用寿命的国内外研究现状,其次详细分析了基于深度学习的剩余寿命预测方法,总结了各个方法的优缺点,最后对未来进行了展望。In recent years,with the continuous development of science and technology,the remaining service life of relay and other electrical components has gradually become a research hotspot.The accurate and efficient analysis of massive monitoring data is the main task of fault detection and health management.Because the traditional methods are limited to rely on the physical models and prior knowledge,the deep learning methods emerge as the times require.First,the current research status of the remaining life of relays at home and abroad is introduced.Second,it analyzes the remaining life prediction method based on deep learning in detail is analyszed,and the advantages and disadvantages of each method are summarized.Finally,the future is looked forward to.

关 键 词：深度学习 寿命预测 神经网络 故障预测与健康管理(PHM) 

分 类 号：TM58[电气工程—电器]