基于深度学习方法的UPS电源预测性维护的物联网系统的开发  

Analysis and Optimization of Induction Motors Based on the Finite Element Method

作　　者：苏君平 李永香 SU Jun-ping;LI Yong-xiang(City Vocational College,Xiamen,Fujian 361008;Xiamen Open University,Xiamen,Fujian 361008;Lanzhou Power Vehicle Research Institute Co.,Ltd.,Lanzhou,Gansu 730050)

机构地区：[1]厦门城市职业学院,福建厦门361008 [2]厦门开放大学,福建厦门361008 [3]兰州电源车辆研究所有限公司,甘肃兰州730050

出　　处：《移动电源与车辆》2024年第4期12-17,共6页Movable Power Station & Vehicle

摘　　要：近年来,随着移动通信基站、数据中心、计算服务器等对供电要求较高的设备的数量增多,UPS电源也得到越来越多的应用。然而UPS的安装分布比较分散,人工巡检的劳动强度高,巡检难度大,对UPS的维护与检修造成了困难。本文提出了一种基于深度学习方法和LoRA通信协议的UPS电源预测性维护物联网技术。该系统通过对UPS电压、电流、环境温度数据的实时采集,利用深度学习模型进行数据分析和预测,从而有效降低运维人员的工作强度,提高系统可靠性,从而实现对UPS电源潜在风险的判断和维护建议推荐。In recent years,with the increase in the number of devices with high power requirements such as mobile communication base stations,data centers,and computing servers,UPS power supplies have been increasingly used.However,the installation distribution of UPS is relatively scattered,and the labor intensity and difficulty of manual inspections are high,which makes the maintenance and inspection of UPS difficult.This paper proposes a UPS power predictive maintenance IoT system based on deep learning methods and LoRA communication protocols.The system collects UPS voltage,current,and ambient temperature data in real time,and uses deep learning models for data analysis and prediction,thereby effectively reducing the workload of operation and maintenance personnel and improving system reliability,thereby realizing the judgment of potential risks of UPS power supplies and recommending maintenance suggestions.

关 键 词：UPS 物联网 深度学习 

分 类 号：TM911[电气工程—电力电子与电力传动]