摘 要
随着工业4.0的推进，自动化生产线在现代制造业中发挥着不可替代的作用，但其复杂性也带来了故障频发的问题，严重影响生产效率和产品质量。通过分析现有故障诊断技术，发现传统方法存在对复杂故障适应性差、诊断速度慢等不足，为此提出基于深度学习与专家系统的融合诊断方法。该方法利用深度学习强大的特征提取能力自动获取故障特征，并结合专家系统丰富的领域知识进行故障推理判断。实验选取某汽车制造企业的自动化生产线为对象，在模拟多种典型故障场景下，将所提方法与传统方法对比测试。结果表明，所提方法能够更快速准确地识别故障类型，平均诊断时间缩短约30%，诊断正确率提高至95%以上。这不仅提高了生产线的故障处理效率，还为实现智能化运维提供了有力支持，为自动化生产线故障诊断技术的发展提供了新思路，推动了智能制造背景下故障诊断技术的进步。
关键词：自动化生产线 故障诊断 深度学习
Abstract
With the advancement of Industry 4.0, automated production lines play an irreplaceable role in modern manufacturing, but their complexity also brings frequent failures, seriously affecting production efficiency and product quality. By analyzing the existing fault diagnosis technology, it is found that the traditional method has some shortcomings such as poor adaptability to complex faults and slow diagnosis speed, so a fusion diagnosis method based on deep learning and expert system is proposed. This method uses the powerful feature extraction ability of deep learning to automatically obtain fault features, and combines the rich domain knowledge of expert system to make fault reasoning judgment. In the experiment, the automatic production line of an automobile manufacturing enterprise is selected as the ob ject, and the proposed method is compared with the traditional method under the simulation of several typical fault scenarios. The results show that the proposed method can identify fault types more quickly and accurately, shorten the average diagnosis time by about 30%, and increase the diagnosis accuracy to more than 95%. This not only improves the fault handling efficiency of the production line, but also provides strong support for the realization of intelligent operation and maintenance, provides a new idea for the development of automatic production line fault diagnosis technology, and promotes the progress of fault diagnosis technology under the background of intelligent manufacturing.
Keyword:automatic production line Fault diagnosis Deep learning
目 录
1绪论 1
1.1研究背景及意义 1
1.2故障诊断技术的研究现状综述 1
1.3本文研究方法与技术路线 1
2故障诊断的关键技术分析 2
2.1数据采集与信号处理技术 2
2.2模型建立与仿真分析方法 2
2.3故障特征提取与模式识别 3
3故障诊断系统设计与实现 4
3.1系统架构设计原则 4
3.2关键模块功能实现 4
3.3系统集成与测试验证 5
4实际应用案例研究 5
4.1典型故障案例分析 6
4.2诊断效果评估指标 6
4.3应用前景与改进建议 6
5结论 7
参考文献 8
致谢 9
