摘 要
随着现代工业的快速发展，机械设备的高效运行和可靠性保障已成为制造业的核心关注点，而关键部件的故障诊断技术在预防性维护和设备健康管理中具有重要意义。本研究旨在针对机械设备关键部件的故障诊断问题，提出一种基于多源信号融合与智能算法的综合诊断方法。通过采集振动、温度、声学等多种传感器数据，结合深度学习模型和传统信号处理技术，构建了高精度的故障特征提取与分类框架。研究创新性地引入了注意力机制优化特征权重分配，并设计了一种自适应阈值策略以提高小样本条件下的诊断准确率。实验结果表明，该方法能够有效识别多种复杂工况下的微弱故障特征，相较于传统方法，其诊断精度提升了约15%，且对早期故障的敏感度显著增强。此外，本研究还开发了一套适用于实际工业场景的在线监测系统，验证了方法的可行性和鲁棒性。总体而言，本研究为机械设备关键部件的智能化故障诊断提供了新思路，同时为工业4.0背景下的预测性维护技术发展奠定了理论与实践基础。关键词：故障诊断；多源信号融合；深度学习；注意力机制；自适应阈值策略
Abstract
With the rapid development of modern industry, ensuring the efficient operation and reliability of mechanical equipment has become a core concern in manufacturing, while fault diagnosis technology for key components plays a significant role in preventive maintenance and equipment health management. This study focuses on the fault diagnosis of critical mechanical components and proposes an integrated diagnostic approach based on multi-source signal fusion and intelligent algorithms. By collecting data from various sensors such as vibration, temperature, and acoustics, and combining deep learning models with traditional signal processing techniques, a high-precision fr amework for fault feature extraction and classification is constructed. Innovatively, this research introduces an attention mechanism to optimize feature weight allocation and designs an adaptive threshold strategy to enhance diagnostic accuracy under small-sample conditions. Experimental results demonstrate that this method can effectively identify weak fault features under complex operating conditions. Compared with traditional methods, the diagnostic accuracy is improved by approximately 15%, with significantly enhanced sensitivity to early faults. Furthermore, a real-time monitoring system applicable to practical industrial scenarios has been developed, verifying the feasibility and robustness of the proposed method. Overall, this study provides new insights into intelligent fault diagnosis for critical components of mechanical equipment and lays a theoretical and practical foundation for the development of predictive maintenance technologies in the context of Industry 4.0..
Key Words：Fault Diagnosis；Multi-Source Signal Fusion；Deep Learning；Attention Mechanism；Adaptive Threshold Strategy
目 录
摘 要 I
Abstract II
第1章 绪论 2
1.1 机械设备关键部件故障诊断技术的研究背景 2
1.2 机械设备关键部件故障诊断技术的研究意义 2
1.3 国内外研究现状与发展趋势 2
1.4 本文研究方法与主要内容 3
第2章 故障诊断技术的基础理论 4
2.1 关键部件故障特征分析方法 4
2.2 常用信号处理技术在诊断中的应用 4
2.3 数据驱动的故障诊断模型构建 5
第3章 关键部件故障诊断的核心技术 7
3.1 振动信号分析与故障模式识别 7
3.2 基于人工智能的故障预测方法 7
3.3 多传感器数据融合在诊断中的作用 8
第4章 实际应用与案例分析 10
4.1 典型机械设备的关键部件诊断流程 10
4.2 工业场景下的故障诊断系统设计 10
4.3 应用效果评估与优化建议 11
结 论 12
参考文献 13
致 谢 14
