摘 要
随着工业4.0的推进和智能制造技术的发展，机械设备故障诊断已成为提升设备可靠性和运行效率的关键环节，传统基于信号处理与特征提取的方法在复杂工况下逐渐显现出局限性，而深度学习技术凭借其强大的非线性映射能力和自动化特征提取能力为故障诊断领域提供了新的解决方案本研究旨在探索基于深度学习的机械设备故障诊断方法，以提高诊断精度和适应性具体而言，本文提出了一种融合卷积神经网络(CNN)与长短时记忆网络(LSTM)的混合模型，该模型能够有效捕捉机械设备的时空特征，并通过多层结构实现对复杂故障模式的深层次表征同时，针对小样本问题，引入了数据增强技术和迁移学习策略，从而显著提升了模型在有限数据条件下的泛化性能实验结果表明，所提方法在多种典型机械设备故障数据集上均取得了优于传统方法的诊断效果，特别是在噪声干扰和工况变化条件下表现出更强的鲁棒性此外，通过对模型内部特征图的可视化分析，进一步验证了其对关键故障特征的有效提取能力综上所述，本研究不仅为机械设备故障诊断提供了一种高效可行的技术手段，还为深度学习在工业领域的应用拓展奠定了理论基础主要贡献在于提出了适用于复杂工况的混合深度学习框架，并解决了小样本训练这一实际工程难题，为后续相关研究提供了有益参考
关键词：深度学习；机械设备故障诊断；卷积神经网络；长短时记忆网络；数据增强
Abstract
With the advancement of Industry 4.0 and the development of intelligent manufacturing technologies, machinery fault diagnosis has become a critical component in enhancing equipment reliability and operational efficiency. Traditional methods based on signal processing and feature extraction are increasingly showing limitations under complex working conditions. In contrast, deep learning techniques, with their powerful nonlinear mapping capabilities and automated feature extraction, offer new solutions for fault diagnosis. This study aims to explore deep learning-based approaches for machinery fault diagnosis to improve diagnostic accuracy and adaptability. Specifically, a hybrid model integrating Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is proposed, which effectively captures spatiotemporal characteristics of mechanical equipment and represents complex fault patterns through a multi-layered structure. To address the issue of small sample sizes, data augmentation techniques and transfer learning strategies are introduced, significantly enhancing the model's generalization performance under limited data conditions. Experimental results demonstrate that the proposed method achieves superior diagnostic performance compared to traditional approaches across multiple typical machinery fault datasets, particularly exhibiting stronger robustness under noisy interference and varying operating conditions. Furthermore, visualization analysis of internal feature maps confirms the model's effective extraction of key fault features. In summary, this research not only provides an efficient and feasible technical means for machinery fault diagnosis but also lays a theoretical foundation for the application of deep learning in industrial domains. The primary contributions include the proposal of a hybrid deep learning fr amework suitable for complex working conditions and the resolution of the practical engineering challenge of small-sample training, offering valuable references for future related studies.
Keywords: Deep Learning; Machinery Fault Diagnosis; Convolutional Neural Network; Long Short-Term Memory Network; Data Augmentation
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状分析 1
1.3本文研究方法概述 2
2深度学习基础理论与应用 2
2.1深度学习基本原理 2
2.2常见深度学习模型综述 3
2.3数据预处理技术在故障诊断中的作用 3
2.4深度学习在机械设备故障诊断中的优势 4
3故障特征提取与深度学习模型构建 4
3.1故障信号特征提取方法 4
3.2深度学习模型选择与优化 5
3.3模型训练策略与参数调整 5
3.4特征提取与模型性能的关系分析 6
4实验验证与结果分析 6
4.1实验设计与数据集构建 7
4.2不同模型的对比实验分析 7
4.3实验结果评估与性能改进 8
4.4深度学习方法的实际应用案例 8
结论 9
参考文献 10
致 谢 11
