摘 要
随着工业4.0的推进和智能制造技术的发展，自动化生产线在现代制造业中占据核心地位，而机电一体化系统的复杂性也使得故障诊断成为保障生产效率与质量的关键环节本研究旨在针对自动化生产线中的机电一体化系统，提出一种基于多源数据融合与智能算法的故障诊断技术，以实现对潜在故障的快速定位、精准分析及有效预测研究结合传感器采集的振动、温度、电流等多源异构数据，采用信号处理方法提取特征信息，并通过深度学习模型构建故障模式识别框架实验结果表明，该方法能够显著提升故障诊断的准确率和实时性，相较于传统方法具有更强的适应性和鲁棒性此外，本研究创新性地引入了边缘计算架构，将部分计算任务迁移至现场设备，从而降低系统延迟并提高响应速度这一技术方案不仅为自动化生产线提供了可靠的故障诊断手段，还为智能制造领域的维护策略优化奠定了理论基础研究成果可广泛应用于各类复杂机电系统，具有重要的工程价值和推广意义关键词：自动化生产线；故障诊断；多源数据融合；深度学习；边缘计算
Abstract
With the advancement of Industry 4.0 and the development of intelligent manufacturing technologies, automated production lines have become a core component of modern manufacturing, while the complexity of mechatronic systems has made fault diagnosis a critical factor in ensuring production efficiency and quality. This study aims to propose a fault diagnosis technique based on multi-source data fusion and intelligent algorithms for mechatronic systems in automated production lines, enabling rapid localization, precise analysis, and effective prediction of potential faults. By integrating multi-source heterogeneous data such as vibration, temperature, and current collected via sensors, this research employs signal processing methods to extract feature information and constructs a fault pattern recognition fr amework using deep learning models. Experimental results demonstrate that this approach significantly improves the accuracy and real-time performance of fault diagnosis compared to traditional methods, exhibiting stronger adaptability and robustness. Additionally, this study innovatively incorporates an edge computing architecture, migrating part of the computational tasks to on-site devices, thereby reducing system latency and enhancing response speed. This technical solution not only provides a reliable fault diagnosis method for automated production lines but also lays a theoretical foundation for optimizing maintenance strategies in the field of intelligent manufacturing. The research findings can be widely applied to various complex mechatronic systems, offering significant engineering value and potential for broad application..
Key Words：Automation Production Line；Fault Diagnosis；Multi-Source Data Fusion；Deep Learning；Edge Computing
目 录
摘 要 I
Abstract II
第1章 绪论 1
1.1 自动化生产线故障诊断的背景与意义 1
1.2 机电一体化故障诊断技术的研究现状 1
1.3 本文研究方法与技术路线 2
第2章 故障诊断技术的基础理论 3
2.1 机电一体化系统的基本构成 3
2.2 故障诊断的核心原理与方法 3
2.3 数据采集与信号处理技术分析 4
2.4 常见故障模式及其特征提取 5
第3章 自动化生产线中的故障检测与识别 6
3.1 生产线故障检测的关键技术 6
3.2 基于传感器网络的故障监测方法 6
3.3 智能算法在故障识别中的应用 7
3.4 实时监控系统的构建与优化 7
3.5 典型案例分析与验证 8
第4章 故障诊断技术的优化与实施策略 9
4.1 提高诊断准确性的关键技术 9
4.2 多源数据融合在故障诊断中的作用 9
4.3 预测性维护与故障预警机制 10
4.4 系统可靠性评估与改进措施 10
4.5 实际应用场景中的挑战与对策 11
结 论 11
参考文献 13
致 谢 14
