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作 者:陈小利 黄戌霞 CHEN Xiaoli;HUANG Xuxia(School of Information Technology and Engineering,Ningde Vocational and Technical College,Fu'an Fujian 355000,China)
机构地区:[1]宁德职业技术学院信息技术与工程学院,福建福安355000
出 处:《九江学院学报(自然科学版)》2024年第2期76-81,共6页Journal of Jiujiang University:Natural Science Edition
基 金:2022年福建省教育厅中青年教师教育科研项目(编号JAT220752);2017年福建省教育厅中青年教师教育科研项目(编号JAT171138)的成果之一
摘 要:随着物联网技术的迅猛发展,物联网设备的故障诊断成为一项关键任务。传统的故障诊断方法在处理大规模、复杂的物联网系统时存在局限性,因此基于深度学习的物联网故障诊断专家系统备受关注。该论文旨在通过优化深度学习算法并评估其性能,提出一种高效准确的物联网故障诊断专家系统。文章设计了一个基于深度学习的专家系统架构,包括数据预处理、模型选择与优化、模型训练与调优等步骤。文章采用了真实的物联网故障数据集进行了实验评估,并使用多种评估指标对系统性能进行了全面分析。实验结果表明,提出的系统在故障诊断准确性、效率和鲁棒性方面都取得了显著的改进。该论文的研究成果对于提升物联网设备故障诊断的效率和准确性具有重要意义,为物联网系统的稳定运行和维护提供了有力支持。With the rapid development of IOT technology,fault diagnosis of IOT devices had become a key task.Traditional fault diagnosis methods had limitations in dealing with large-scale and complex IOT systems.Therefore,the IOT fault diagnosis expert system based on deep learning has attracted much attention.This paper aimed to propose an efficient and accurate IOT fault diagnosis expert system by optimizing the deep learning algorithm and evaluating its performance.This paper designed an expert system architecture based on deep learning,including data pre-processing,model selection and optimization,model training and tuning and so on.In this paper,real IOT fault datasets were used for experimental evaluation,and a variety of evaluation indicators were used to analyze the system performance comprehensively.The experimental results showed that the proposed system has achieved significant improvements in fault diagnosis accuracy,efficiency and robustness.The research results of this paper were of great significance for improving the efficiency and accuracy of fault diagnosis of IOT devices,and provide strong support for the stable operation and maintenance of IOT systems.
分 类 号:U495[交通运输工程—交通运输规划与管理]
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