摘 要
随着工业4.0的推进和智能制造技术的发展，机械装备的高效运行与智能化管理成为提升生产效率和降低维护成本的关键。针对传统机械装备运维模式中信息孤岛严重、故障响应滞后及资源利用率低等问题，本研究提出了一种基于物联网和云计算的远程监控与运维管理系统。该系统通过集成传感器网络、边缘计算和大数据分析技术，实现了对机械装备运行状态的实时监测、故障诊断以及预测性维护。研究采用分层架构设计，包括数据采集层、通信传输层、云端处理层和用户交互层，确保了系统的灵活性与可扩展性。实验结果表明，该系统能够显著提高故障检测的准确率，减少非计划停机时间，并优化维护资源的分配。
关键词：智能制造 远程监控与运维管理 边缘计算
Abstract
With the promotion of industry 4.0 and the development of intelligent manufacturing technology, the efficient operation and intelligent management of machinery and equipment have become the key to improve production efficiency and reduce maintenance costs. In view of the problems of serious information island, lagging fault response and low resource utilization in the traditional operation and maintenance mode of machinery and equipment, this study proposes a remote monitoring and operation and maintenance management system based on the Internet of Things and cloud computing. By integrating sensor network, edge computing and big data analysis technology, the system realizes real-time monitoring, fault diagnosis and predictive maintenance of mechanical equipment. The study adopts a hierarchical architecture design, including data acquisition layer, communication transmission layer, cloud processing layer and user interaction layer, to ensure the flexibility and scalability of the system. The experimental results show that the system can significantly improve the accuracy of fault detection, reduce unplanned downtime, and optimize the allocation of maintenance resources.
Keyword:Intelligent Manufacturing Remote Monitoring And Operation Maintenance Management
目 录
1绪论 1
1.1机械装备远程监控的研究背景与意义 1
1.2国内外研究现状分析 1
1.3研究方法与技术路线 2
2远程监控系统的关键技术 2
2.1数据采集与传输技术 3
2.2实时监测与故障诊断技术 3
2.3数据存储与管理技术 4
3运维管理系统的功能设计 4
3.1系统架构与模块划分 4
3.2故障预警与维护计划制定 5
3.3数据可视化与用户交互设计 5
4系统实现与应用案例分析 6
4.1系统开发环境与工具选择 6
4.2实验验证与性能评估 6
4.3应用案例与效果分析 7
结论 7
参考文献 9
致谢 10
随着工业4.0的推进和智能制造技术的发展，机械装备的高效运行与智能化管理成为提升生产效率和降低维护成本的关键。针对传统机械装备运维模式中信息孤岛严重、故障响应滞后及资源利用率低等问题，本研究提出了一种基于物联网和云计算的远程监控与运维管理系统。该系统通过集成传感器网络、边缘计算和大数据分析技术，实现了对机械装备运行状态的实时监测、故障诊断以及预测性维护。研究采用分层架构设计，包括数据采集层、通信传输层、云端处理层和用户交互层，确保了系统的灵活性与可扩展性。实验结果表明，该系统能够显著提高故障检测的准确率，减少非计划停机时间，并优化维护资源的分配。
关键词：智能制造 远程监控与运维管理 边缘计算
Abstract
With the promotion of industry 4.0 and the development of intelligent manufacturing technology, the efficient operation and intelligent management of machinery and equipment have become the key to improve production efficiency and reduce maintenance costs. In view of the problems of serious information island, lagging fault response and low resource utilization in the traditional operation and maintenance mode of machinery and equipment, this study proposes a remote monitoring and operation and maintenance management system based on the Internet of Things and cloud computing. By integrating sensor network, edge computing and big data analysis technology, the system realizes real-time monitoring, fault diagnosis and predictive maintenance of mechanical equipment. The study adopts a hierarchical architecture design, including data acquisition layer, communication transmission layer, cloud processing layer and user interaction layer, to ensure the flexibility and scalability of the system. The experimental results show that the system can significantly improve the accuracy of fault detection, reduce unplanned downtime, and optimize the allocation of maintenance resources.
Keyword:Intelligent Manufacturing Remote Monitoring And Operation Maintenance Management
目 录
1绪论 1
1.1机械装备远程监控的研究背景与意义 1
1.2国内外研究现状分析 1
1.3研究方法与技术路线 2
2远程监控系统的关键技术 2
2.1数据采集与传输技术 3
2.2实时监测与故障诊断技术 3
2.3数据存储与管理技术 4
3运维管理系统的功能设计 4
3.1系统架构与模块划分 4
3.2故障预警与维护计划制定 5
3.3数据可视化与用户交互设计 5
4系统实现与应用案例分析 6
4.1系统开发环境与工具选择 6
4.2实验验证与性能评估 6
4.3应用案例与效果分析 7
结论 7
参考文献 9
致谢 10
