摘 要
随着电力系统规模的不断扩大和复杂程度的日益增加，电气设备的安全稳定运行对整个电力系统的可靠性和经济性至关重要，而传统故障诊断方法存在检测精度低、实时性差等问题。基于超声波检测的电气设备故障诊断旨在利用超声波在不同介质中传播特性差异来实现对电气设备内部结构损伤及故障的有效识别。本研究以变压器、断路器等典型电气设备为对象，通过建立超声波与电气设备故障特征之间的映射关系，提出一种融合多源信息的超声波检测新方法。该方法采用高灵敏度超声传感器阵列获取设备运行时产生的超声信号，运用小波变换等时频分析技术提取信号特征，并结合深度学习算法构建故障模式识别模型，实现了对多种故障类型的精准分类。实验结果表明，此方法能够准确检测出局部放电、机械部件磨损等常见故障，且具有较高的检测灵敏度和抗干扰能力。
关键词：超声波检测 电气设备故障诊断 多源信息融合
Abstract
As the scale and complexity of power systems continue to expand, the safe and stable operation of electrical equipment has become critical to the reliability and economic efficiency of the entire power system. Traditional fault diagnosis methods suffer from issues such as low detection accuracy and poor real-time performance. Ultrasonic-based fault diagnosis for electrical equipment aims to identify internal structural damage and faults effectively by leveraging the differences in ultrasonic wave propagation characteristics across different media. This study focuses on typical electrical equipment such as transformers and circuit breakers, establishing a mapping relationship between ultrasonic signals and fault features of electrical equipment. A novel ultrasonic detection method that integrates multi-source information is proposed. This method employs a high-sensitivity ultrasonic sensor array to capture ultrasonic signals generated during equipment operation, utilizes time-frequency analysis techniques such as wavelet transform to extract signal features, and combines deep learning algorithms to construct a fault pattern recognition model, achieving precise classification of various fault types. Experimental results demonstrate that this method can accurately detect common faults such as partial discharge and mechanical component wear, exhibiting high detection sensitivity and strong anti-interference capability.
Keyword:Ultrasonic Testing Electrical Equipment Fault Diagnosis Multi-source Information Fusion
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 1
1.3本文研究方法 2
2超声波检测原理与技术 2
2.1超声波物理特性 2
2.2检测信号处理方法 3
2.3超声波传感器选型 3
3电气设备故障特征分析 4
3.1典型故障模式识别 4
3.2故障超声波信号特征 5
3.3故障诊断模型构建 5
4实验验证与应用案例 6
4.1实验平台搭建 6
4.2实测数据分析 7
4.3应用效果评估 7
结论 8
参考文献 10
致谢 11
