摘 要
随着工业4.0的推进，机械设备的智能化管理和高效运维成为现代制造业发展的关键需求，而基于物联网的远程监测与诊断技术为解决这一问题提供了新思路。本研究旨在通过融合物联网、大数据分析及人工智能算法，构建一套适用于复杂工况下机械设备的远程监测与故障诊断系统，以提升设备运行可靠性并降低维护成本。研究采用分层架构设计，底层通过传感器网络实现数据采集与传输，中层利用边缘计算对数据进行预处理和特征提取，高层则结合机器学习模型完成故障模式识别与预测分析。实验结果表明，该系统能够实时获取设备运行状态，并在多种典型故障场景下达到95%以上的诊断准确率。此外，相较于传统方法，本研究提出的优化算法显著提升了诊断效率与鲁棒性，尤其在噪声干扰环境下表现出更强的适应能力。
关键词：物联网 远程监测与诊断 机器学习
Abstract
With the promotion of Industry 4.0, the intelligent management and efficient operation and maintenance of mechanical equipment have become the key demand of the development of modern manufacturing industry, and the remote monitoring and diagnosis technology based on the Internet of Things provides a new idea to solve this problem. This study aims to build a set of remote monitoring and fault diagnosis system suitable for mechanical equipment under complex working conditions by integrating the Internet of Things, big data analysis and artificial intelligence algorithm, so as to improve the operation reliability of the equipment and reduce the maintenance cost. The research adopts the hierarchical architecture design, which realizes data collection and transmission through the sensor network, the middle level uses edge computing to conduct data pre-processing and feature extraction, and the top level combines the machine learning model to complete the failure mode recognition and predictive analysis. The experimental results show that the system can obtain the running state of the device in real time and achieve the diagnostic accuracy of more than 95% in many typical fault scenarios. In addition, compared with the traditional method, the optimization algorithm proposed in this study significantly improves the diagnostic efficiency and robustness, especially showing stronger adaptability in the noise interference environment.
Keyword:Internet Of Things Remote Monitoring And Diagnosis Machine Learning
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状分析 1
1.3本文研究方法概述 2
2物联网技术在机械设备监测中的应用基础 2
2.1物联网技术的核心原理 2
2.2机械设备监测的数据采集方法 3
2.3数据传输与存储的关键技术 3
3远程监测系统的设计与实现 4
3.1系统架构设计原则 4
3.2数据处理与分析方法 4
3.3实时监测功能的实现 5
4基于物联网的设备故障诊断技术研究 5
4.1故障诊断的基本理论 5
4.2数据驱动的诊断模型构建 6
4.3诊断结果的评估与优化 7
结论 7
参考文献 8
致谢 9
随着工业4.0的推进，机械设备的智能化管理和高效运维成为现代制造业发展的关键需求，而基于物联网的远程监测与诊断技术为解决这一问题提供了新思路。本研究旨在通过融合物联网、大数据分析及人工智能算法，构建一套适用于复杂工况下机械设备的远程监测与故障诊断系统，以提升设备运行可靠性并降低维护成本。研究采用分层架构设计，底层通过传感器网络实现数据采集与传输，中层利用边缘计算对数据进行预处理和特征提取，高层则结合机器学习模型完成故障模式识别与预测分析。实验结果表明，该系统能够实时获取设备运行状态，并在多种典型故障场景下达到95%以上的诊断准确率。此外，相较于传统方法，本研究提出的优化算法显著提升了诊断效率与鲁棒性，尤其在噪声干扰环境下表现出更强的适应能力。
关键词：物联网 远程监测与诊断 机器学习
Abstract
With the promotion of Industry 4.0, the intelligent management and efficient operation and maintenance of mechanical equipment have become the key demand of the development of modern manufacturing industry, and the remote monitoring and diagnosis technology based on the Internet of Things provides a new idea to solve this problem. This study aims to build a set of remote monitoring and fault diagnosis system suitable for mechanical equipment under complex working conditions by integrating the Internet of Things, big data analysis and artificial intelligence algorithm, so as to improve the operation reliability of the equipment and reduce the maintenance cost. The research adopts the hierarchical architecture design, which realizes data collection and transmission through the sensor network, the middle level uses edge computing to conduct data pre-processing and feature extraction, and the top level combines the machine learning model to complete the failure mode recognition and predictive analysis. The experimental results show that the system can obtain the running state of the device in real time and achieve the diagnostic accuracy of more than 95% in many typical fault scenarios. In addition, compared with the traditional method, the optimization algorithm proposed in this study significantly improves the diagnostic efficiency and robustness, especially showing stronger adaptability in the noise interference environment.
Keyword:Internet Of Things Remote Monitoring And Diagnosis Machine Learning
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状分析 1
1.3本文研究方法概述 2
2物联网技术在机械设备监测中的应用基础 2
2.1物联网技术的核心原理 2
2.2机械设备监测的数据采集方法 3
2.3数据传输与存储的关键技术 3
3远程监测系统的设计与实现 4
3.1系统架构设计原则 4
3.2数据处理与分析方法 4
3.3实时监测功能的实现 5
4基于物联网的设备故障诊断技术研究 5
4.1故障诊断的基本理论 5
4.2数据驱动的诊断模型构建 6
4.3诊断结果的评估与优化 7
结论 7
参考文献 8
致谢 9
