摘 要
随着智能制造技术的快速发展，数控机床作为现代制造业的核心设备，其运行可靠性直接影响生产效率和产品质量。针对传统故障诊断方法在复杂工况下准确率不足的问题，本研究提出了一种基于深度学习的数控机床智能故障诊断方法。通过构建多源异构数据融合模型，整合振动信号、温度数据和加工参数等多维度信息，有效提升了特征提取的全面性。在算法设计方面，创新性地将改进的卷积神经网络与长短期记忆网络相结合，设计了具有自适应特征学习能力的混合深度学习模型。实验结果表明，该方法在典型故障样本集上的平均诊断准确率达到96.8%，较传统方法提升约15%。同时，通过引入注意力机制和迁移学习策略，显著提高了模型对未知故障类型的泛化能力。研究还开发了相应的在线监测系统原型，实现了故障的实时预警与诊断。本研究成果为数控机床的智能化运维提供了新的技术路径，对提升制造系统的可靠性和生产效率具有重要意义。
关键词：数控机床 智能故障诊断 深度学习
Abstract
With the rapid development of intelligent manufacturing technology, CNC machine tools as the core equipment of modern manufacturing industry, its operational reliability directly affects production efficiency and product quality. Aiming at the problem that the accuracy of traditional fault diagnosis methods is insufficient under complex working conditions, this paper proposes an intelligent fault diagnosis method for CNC machine tools based on deep learning. By constructing a multi-source heterogeneous data fusion model and integrating multi-dimensional information such as vibration signal, temperature data and processing parameters, the comprehensiveness of feature extraction is effectively improved. In terms of algorithm design, the improved convolutional neural network is innovatively combined with long short-term memory network to design a hybrid deep learning model with adaptive feature learning ability. The experimental results show that the average diagnostic accuracy of the proposed method on the typical fault sample set reaches 96.8%, which is about 15% higher than that of the traditional method. At the same time, by introducing attention mechanism and transfer learning strategy, the generalization ability of the model to unknown fault types is significantly improved. The prototype of online monitoring system is also developed to realize real-time fault warning and diagnosis. The research results provide a new technical path for the intelligent operation and maintenance of CNC machine tools, and have important significance for improving the reliability and production efficiency of manufacturing system.
Keyword:CNC machine tool Intelligent fault diagnosis Deep learning
目 录
1绪论 1
1.1研究背景及意义 1
1.2深度学习在故障诊断中的应用现状 1
2数控机床故障特征提取方法研究 1
2.1基于振动信号的故障特征分析 1
2.2多源数据融合的特征提取方法 2
2.3深度特征学习模型构建与优化 3
3基于深度学习的故障诊断模型研究 3
3.1CNN在数控机床故障诊断中的应用 3
3.2LSTM网络在时序数据分析中的应用 4
3.3混合深度学习模型的构建与验证 5
4故障诊断系统实现与性能评估 5
4.1实验平台搭建与数据采集 5
4.2诊断系统架构设计与实现 6
4.3系统性能评估与对比分析 6
5结论 7
参考文献 8
致谢 9
