摘 要
随着工业4.0的推进和物联网技术的快速发展，机械装备的智能化管理成为提升制造业效率与可靠性的关键方向。本研究旨在通过构建基于物联网的远程监控与管理系统，实现对机械装备运行状态的实时感知、数据分析及智能决策支持。研究结合传感器网络、云计算及大数据分析技术，设计了一种多层次架构体系，包括数据采集层、通信传输层、云端处理层以及用户交互层，从而有效解决传统管理模式中信息孤岛和响应滞后的问题。创新性地引入了边缘计算以优化数据处理效率，并开发了基于机器学习的状态预测模型，显著提高了故障预警的准确性和及时性。实验结果表明，该系统能够稳定采集并传输装备运行数据，其预测精度达到95%以上，同时大幅降低了维护成本和停机时间。此外，本研究还提出了适用于复杂工况的数据清洗与特征提取方法，为多类型机械装备的兼容性管理提供了技术支持。最终结论显示，基于物联网的远程监控与管理方案不仅提升了装备运维水平，还为智能制造背景下的数字化转型提供了可行路径，具有重要的理论价值和实际应用前景。关键词：工业4.0；物联网；远程监控与管理；边缘计算；机器学习状态预测模型
Abstract
With the advancement of Industry 4.0 and the rapid development of Internet of Things (IoT) technology, intelligent management of mechanical equipment has become a critical direction for enhancing efficiency and reliability in manufacturing. This study aims to construct an IoT-based remote monitoring and management system to achieve real-time perception of equipment operating conditions, data analysis, and intelligent decision support. By integrating sensor networks, cloud computing, and big data analytics, a multi-layered architecture was designed, comprising a data acquisition layer, communication transmission layer, cloud processing layer, and user interaction layer, effectively addressing issues of information silos and delayed responses in traditional management approaches. Innovatively, edge computing was introduced to optimize data processing efficiency, and a machine-learning-based condition prediction model was developed, significantly improving the accuracy and timeliness of fault warnings. Experimental results demonstrate that the system can stably collect and transmit equipment operation data, achieving a prediction accuracy of over 95%, while substantially reducing maintenance costs and downtime. Furthermore, this research proposes a data cleaning and feature extraction method suitable for complex working conditions, providing technical support for the compatibility management of various types of mechanical equipment. The final conclusion indicates that the IoT-based remote monitoring and management solution not only enhances equipment maintenance levels but also offers a feasible pathway for digital transformation under the context of smart manufacturing, possessing significant theoretical value and practical application prospects..Key Words：Industry 4.0；Internet Of Things；Remote Monitoring And Management；Edge Computing；Machine Learning Condition Prediction Model
目 录
摘 要 I
Abstract II
第1章 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 本文研究方法与技术路线 2
第2章 物联网技术在机械装备监控中的应用基础 3
2.1 物联网关键技术概述 3
2.2 机械装备监控的数据采集需求 3
2.3 数据传输与通信协议的选择 4
2.4 数据存储与管理方案设计 4
第3章 机械装备远程管理的优化策略 6
3.1 基于数据分析的故障预测模型 6
3.2 设备状态评估与健康管理 6
3.3 远程运维流程优化设计 7
3.4 用户需求驱动的服务模式创新 8
结 论 9
参考文献 10
致 谢 11
