摘 要
随着现代工业的快速发展，电气设备在各类生产系统中的应用日益广泛，其运行状态直接关系到系统的安全性和可靠性。然而，电气设备在长期运行过程中难免出现故障，而振动信号作为反映设备运行状态的重要指标，已成为故障诊断领域的关键研究对象。本研究旨在通过分析电气设备的振动信号特征，建立一套高效、精准的故障诊断方法，以实现对设备潜在故障的早期预警和准确识别。为此，本文提出了一种基于时频域分析与智能算法相结合的综合诊断框架。首先，利用小波变换提取振动信号的多尺度特征，捕捉设备在不同运行状态下的细微变化；其次，结合支持向量机（SVM）和深度学习模型，构建了分类器以区分正常与异常工况，并进一步识别具体故障类型。实验结果表明，该方法能够有效提升故障诊断的准确率和鲁棒性，尤其在复杂工况和噪声干扰条件下表现出显著优势。此外，本研究还开发了一种自适应阈值设定策略，用于动态调整诊断模型的灵敏度，从而减少误报率。总体而言，本研究不仅为电气设备的故障诊断提供了新的技术手段，还为智能化运维体系的构建奠定了理论基础，具有重要的学术价值和工程应用前景。关键词：电气设备故障诊断；振动信号分析；时频域特征提取；支持向量机；深度学习模型
Abstract
With the rapid development of modern industry, electrical equipment has been increasingly applied in various production systems, and its operational status directly affects the safety and reliability of these systems. However, faults in electrical equipment are inevitable during long-term operation, and vibration signals, as critical indicators reflecting operational conditions, have become a key focus in fault diagnosis research. This study aims to analyze the characteristics of vibration signals from electrical equipment to establish an efficient and precise fault diagnosis method for early warning and accurate identification of potential faults. To achieve this, a comprehensive diagnostic fr amework combining time-frequency domain analysis with intelligent algorithms is proposed. Firstly, wavelet transform is utilized to extract multi-scale features of vibration signals, capturing subtle changes under different operating conditions. Secondly, a classifier is constructed by integrating support vector machines (SVM) and deep learning models to distinguish between normal and abnormal conditions and further identify specific fault types. Experimental results demonstrate that this method significantly improves the accuracy and robustness of fault diagnosis, particularly under complex operating conditions and in the presence of noise interference. Additionally, an adaptive threshold setting strategy is developed to dynamically adjust the sensitivity of the diagnostic model, thereby reducing false alarm rates. Overall, this study not only provides new technical means for fault diagnosis in electrical equipment but also lays a theoretical foundation for the construction of intelligent maintenance systems, showcasing significant academic value and engineering application potential..
Key Words：Electrical Equipment Fault Diagnosis；Vibration Signal Analysis；Time-Frequency Domain Feature Extraction；Support Vector Machine；Deep Learning Model
目 录
摘 要 I
Abstract II
第1章 绪论 1
1.1 电气设备振动信号分析的研究背景 1
1.2 振动信号分析与故障诊断的意义 1
1.3 国内外研究现状综述 2
1.4 本文研究方法与技术路线 2
第2章 振动信号采集与预处理 3
2.1 振动信号采集系统设计 3
2.2 数据采集中的关键问题分析 3
2.3 振动信号的去噪与增强方法 4
2.4 特征提取与信号重构技术 4
2.5 实验验证与结果分析 5
第3章 故障特征提取与模式识别 6
3.1 基于时域分析的特征提取方法 6
3.2 频域分析在故障诊断中的应用 6
3.3 小波变换与多尺度分析技术 7
3.4 机器学习在模式识别中的作用 7
3.5 特征选择与优化算法研究 8
第4章 故障诊断模型构建与应用 9
4.1 数据驱动的故障诊断模型构建 9
4.2 基于人工智能的诊断方法研究 9
4.3 实时监测系统的开发与实现 10
4.4 典型故障案例分析与验证 10
4.5 系统性能评估与改进方向 11
结 论 12
参考文献 13
致 谢 14
