摘 要
随着工业4.0的推进和智能制造技术的发展，机械自动化生产线在现代制造业中占据核心地位，其高效运行对生产效率和产品质量至关重要然而，设备故障可能导致停机、产品缺陷甚至重大经济损失因此，构建高效的故障诊断与预警系统成为保障生产线稳定性的关键本研究旨在设计并实现一种基于多源数据融合与智能算法的故障诊断与预警系统，以提升自动化生产线的可靠性与智能化水平具体而言，研究首先通过传感器网络采集生产线中的振动、温度、电流等多维数据，并结合信号处理技术提取特征信息随后，引入深度学习模型与传统统计方法相结合的混合诊断策略，以提高故障识别的准确性和实时性此外，研究还开发了基于预测分析的预警模块，能够根据历史数据和当前状态评估潜在风险并生成预警信号实验结果表明，该系统能够在复杂工况下有效识别多种典型故障类型，同时具备较高的预警精度和鲁棒性相比现有方法，本研究的创新点在于融合了多源异构数据的优势，并通过优化算法提升了系统的适应性和泛化能力最终，研究成果为机械自动化生产线的智能化运维提供了新思路，具有重要的理论意义和应用价值关键词：故障诊断；多源数据融合；智能算法；预警系统；深度学习模型
Abstract
With the advancement of Industry 4.0 and the development of intelligent manufacturing technologies, mechanical automated production lines have become central to modern manufacturing, and their efficient operation is critical to production efficiency and product quality. However, equipment failures can lead to downtime, product defects, and even significant economic losses. Therefore, constructing an effective fault diagnosis and early warning system is essential for ensuring the stability of production lines. This study aims to design and implement a fault diagnosis and early warning system based on multi-source data fusion and intelligent algorithms to enhance the reliability and intelligence level of automated production lines. Specifically, the research involves collecting multidimensional data such as vibration, temperature, and current from production lines through a sensor network and extracting feature information using signal processing techniques. Subsequently, a hybrid diagnostic strategy combining deep learning models with traditional statistical methods is introduced to improve the accuracy and real-time performance of fault identification. Additionally, a predictive analysis-based early warning module is developed to evaluate potential risks and generate warning signals according to historical data and current conditions. Experimental results demonstrate that the system can effectively identify various typical fault types under complex operating conditions while achieving high early warning precision and robustness. Compared with existing methods, the innovation of this study lies in leveraging the advantages of multi-source heterogeneous data and enhancing the adaptability and generalization capability of the system through optimized algorithms. Ultimately, the research findings provide new insights into the intelligent maintenance of mechanical automated production lines, offering significant theoretical implications and practical value..
Key Words：Fault Diagnosis；Multi-Source Data Fusion；Intelligent Algorithm；Early Warning System；Deep Learning Model
目 录
摘 要 I
Abstract II
第1章 绪论 1
1.1 机械自动化生产线故障诊断的研究背景 1
1.2 故障诊断与预警系统的重要意义 1
1.3 国内外研究现状与发展趋势 2
1.4 本文研究方法与技术路线 2
第2章 故障诊断系统的理论基础 3
2.1 故障诊断的基本概念与原理 3
2.2 数据采集与信号处理技术 3
2.3 常见故障模式及特征分析 4
2.4 故障诊断算法的分类与选择 5
2.5 理论基础在实际应用中的挑战 5
第3章 预警系统的构建与优化 7
3.1 预警系统的设计原则与目标 7
3.2 实时数据监测与异常检测方法 7
3.3 预警阈值的设定与调整策略 8
3.4 预警模型的建立与验证过程 8
3.5 提高预警系统可靠性的关键技术 9
第4章 故障诊断与预警系统的集成与应用 10
4.1 系统集成的技术框架与实现路径 10
4.2 自动化生产线中的典型应用场景 10
4.3 故障诊断与预警系统的性能评估 11
4.4 系统运行中的问题与改进措施 11
4.5 案例分析：某企业实施效果评估 12
结 论 13
参考文献 14
致 谢 15
