摘 要
振动分析作为机械故障诊断的重要手段，近年来在工业设备健康监测中发挥了关键作用。随着现代工业对设备可靠性和安全性要求的不断提高，传统的故障诊断方法已难以满足复杂系统的需求，因此亟需开发更为精确和高效的诊断技术。本研究旨在通过深入探讨振动信号特征提取与模式识别方法，构建一种基于多源信息融合的智能故障诊断框架，以提升诊断精度和效率。研究采用先进的信号处理算法，包括时域、频域及小波变换等多维分析方法，结合机器学习模型对典型机械部件（如轴承、齿轮）的故障类型和程度进行精准识别。实验结果表明，所提出的框架能够有效克服噪声干扰和数据不完整等问题，显著提高故障诊断的准确率。此外，本研究创新性地引入了深度学习技术优化特征提取过程，并提出了一种自适应阈值设定方法以适应不同工况条件下的诊断需求。总体而言，该研究不仅为机械故障诊断提供了新的理论支持和技术路径，还为智能化工业维护系统的开发奠定了坚实基础，具有重要的工程应用价值和推广前景。关键词：振动信号特征提取；多源信息融合；深度学习；故障诊断精度；自适应阈值设定
Abstract
Vibration analysis, as a critical tool for mechanical fault diagnosis, has played a key role in the health monitoring of industrial equipment in recent years. With the increasing demands for reliability and safety in modern industry, traditional fault diagnosis methods have become insufficient to address the complexities of advanced systems, thus necessitating the development of more accurate and efficient diagnostic techniques. This study focuses on exploring vibration signal feature extraction and pattern recognition methods to construct an intelligent fault diagnosis fr amework based on multi-source information fusion, aiming to enhance diagnostic accuracy and efficiency. Advanced signal processing algorithms, including time-domain, frequency-domain, and wavelet transform-based multidimensional analysis methods, are employed in conjunction with machine learning models to precisely identify fault types and severity levels in typical mechanical components such as bearings and gears. Experimental results demonstrate that the proposed fr amework effectively overcomes issues related to noise interference and incomplete data, significantly improving the accuracy of fault diagnosis. Furthermore, this research innovatively incorporates deep learning technology to optimize the feature extraction process and proposes an adaptive threshold setting method to accommodate diagnostic requirements under varying operating conditions. Overall, this study not only provides new theoretical support and technical approaches for mechanical fault diagnosis but also lays a solid foundation for the development of intelligent industrial maintenance systems, showcasing substantial engineering application value and promising prospects for widespread adoption..
Key Words：Vibration Signal Feature Extraction；Multi-Source Information Fusion；Deep Learning；Fault Diagnosis Accuracy；Adaptive Threshold Setting
目 录
摘 要 I
Abstract II
第1章 绪论 2
1.1 振动分析与机械故障诊断的背景意义 2
1.2 国内外研究现状综述 2
1.3 本文研究方法与技术路线 3
第2章 振动信号采集与处理技术 4
2.1 振动信号采集原理与方法 4
2.2 数据预处理技术分析 4
2.3 特征提取与信号增强方法 5
第3章 振动分析中的故障特征识别 6
3.1 故障特征的数学建模 6
3.2 频域分析在故障诊断中的应用 6
3.3 时频分析方法及其优势 7
第4章 振动分析的实际应用案例研究 8
4.1 典型机械部件的振动特性分析 8
4.2 基于振动分析的故障预测模型 8
4.3 实际工程中的应用效果评估 9
结 论 9
参考文献 11
致 谢 12
