摘 要
随着工业4.0的推进，机电设备的高效稳定运行对现代制造业至关重要。传统维护方式存在滞后性和盲目性，难以满足智能化生产需求。为此，本文基于大数据技术，提出一种面向机电设备的预测性维护策略，旨在通过数据驱动的方法实现设备故障的早期预警与精准维护。研究采用数据挖掘、机器学习等方法，从海量运行数据中提取特征参数，构建了机电设备健康状态评估模型，并引入深度学习算法优化预测精度。通过对某大型制造企业实际生产线的数据分析，验证了该方法的有效性。结果表明，相比传统维护模式，新策略可将设备故障率降低32%，非计划停机时间减少45%。创新点在于融合多源异构数据，实现了跨平台数据整合与实时监控；同时建立了自适应更新机制，确保模型持续优化。该研究为机电设备的智能化维护提供了理论依据和技术支持，对提升制造业整体运维水平具有重要意义。
关键词：机电设备预测性维护；大数据技术；健康状态评估模型
Abstract
With the advancement of Industry 4.0, the efficient and stable operation of electromechanical equipment has become critical to modern manufacturing. Traditional maintenance approaches suffer from lag and盲目ness, failing to meet the demands of intelligent production. To address this issue, this paper proposes a predictive maintenance strategy for electromechanical equipment based on big data technology, aiming to achieve early warning and precise maintenance through data-driven methods. By employing data mining and machine learning techniques, characteristic parameters are extracted from massive operational data to construct a health status evaluation model for electromechanical equipment, and deep learning algorithms are introduced to optimize prediction accuracy. The effectiveness of this method is validated through data analysis from an actual production line of a large manufacturing enterprise. Results show that compared with traditional maintenance models, the new strategy can reduce equipment failure rates by 32% and unplanned downtime by 45%. Innovations lie in integrating multi-source heterogeneous data to realize cross-platform data consolidation and real-time monitoring, while establishing an adaptive updating mechanism to ensure continuous model optimization. This study provides theoretical basis and technical support for the intelligent maintenance of electromechanical equipment, significantly enhancing the overall operation and maintenance level of manufacturing industries.
Key Words：Predictive Maintenance Of Electro-Mechanical Equipment；Big Data Technology；Health Status Evaluation 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 1
1.3 研究方法与技术路线 2
第2章 大数据在机电设备维护中的应用基础 3
2.1 机电设备运行数据采集 3
2.2 数据预处理与特征提取 3
2.3 数据存储与管理方案 4
第3章 预测性维护模型构建与优化 6
3.1 常用预测算法分析 6
3.2 模型选择与参数调优 6
3.3 模型验证与性能评估 7
第4章 预测性维护策略实施与案例分析 9
4.1 维护策略制定原则 9
4.2 实施过程与效果评价 10
4.3 典型案例分析总结 10
结 论 12
参考文献 13
致 谢 14
