摘要
随着工业4.0的推进和物联网技术的快速发展，传统工业设备维护模式已难以满足现代制造业对高效、可靠运行的需求。本研究旨在探索基于物联网的工业设备预防性维护策略，以实现设备故障的早期预警和寿命预测，从而降低非计划停机时间和维护成本。研究通过构建物联网感知层、网络层与应用层的完整架构，实现了对设备运行状态的实时监测和大数据采集。在此基础上，采用机器学习算法对历史数据进行深度分析，建立了设备健康状态评估模型，并结合预测性分析技术生成动态维护计划。实验结果表明，该策略能够显著提升设备维护的精准性和时效性，故障预测准确率超过90%，同时将维护成本降低了约25%。本研究的主要创新点在于将物联网技术和智能化算法深度融合，提出了一种可扩展性强且适应多场景的预防性维护框架，为工业设备管理提供了新的思路和技术支撑。研究成果对推动智能制造发展和优化工业生产流程具有重要意义。
关键词：物联网；预防性维护；机器学习；设备健康状态评估；预测性分析技术
Abstract
With the advancement of Industry 4.0 and the rapid development of Internet of Things (IoT) technology, traditional industrial equipment maintenance models are increasingly unable to meet the demands of modern manufacturing for efficient and reliable operations. This study aims to explore an IoT-based preventive maintenance strategy for industrial equipment to achieve early fault warnings and lifespan predictions, thereby reducing unplanned downtime and maintenance costs. By constructing a comprehensive architecture encompassing the IoT perception layer, network layer, and application layer, real-time monitoring of equipment operational status and large-scale data collection were realized. Based on this, machine learning algorithms were employed to conduct in-depth analyses of historical data, establishing a model for assessing equipment health conditions. Combined with predictive analytics techniques, dynamic maintenance schedules were generated. Experimental results indicate that this strategy significantly enhances the precision and timeliness of equipment maintenance, achieving a fault prediction accuracy rate exceeding 90% while reducing maintenance costs by approximately 25%. The primary innovation of this research lies in the deep integration of IoT technology and intelligent algorithms, proposing a highly scalable and adaptable preventive maintenance fr amework suitable for multiple scenarios. This fr amework provides new insights and technical support for industrial equipment management. The findings hold significant implications for advancing smart manufacturing and optimizing industrial production processes.
Keywords：Internet Of Things; Preventive Maintenance; Machine Learning; Equipment Health Condition Assessment; Predictive Analytics Technology
目 录
摘要 I
Abstract II
一、绪论 1
(一) 工业设备预防性维护的背景与意义 1
(二) 物联网在预防性维护中的研究现状 1
(三) 本文研究方法与技术路线 2
二、物联网技术在工业设备监测中的应用 2
(一) 工业设备数据采集的关键技术 2
(二) 数据传输与网络架构设计 3
(三) 实时监测系统的构建与优化 3
三、预防性维护策略的设计与实施 4
(一) 基于物联网的状态评估模型 4
(二) 故障预测算法的选择与改进 5
(三) 维护计划的制定与执行 5
四、案例分析与效果评估 6
(一) 典型工业场景的应用实践 6
(二) 预防性维护策略的效果验证 6
(三) 系统性能优化与改进建议 7
结 论 8
参考文献 9
