高压断路器弹机制的小样本故障诊断方法,基于多源特征融合和堆叠合奏学习
Xining Li1, Hanyan Xiao1, Ke Zhao1
1State Grid Jiangsu Electric Power Research Institute, Nanjing 211103, China.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
本研究引入了一种新的小样本故障诊断方法,用于使用多源特征融合和堆叠集体学习的高压断路器. 该方法实现了96.1%的准确性,提高了设备的维护.
科学领域:
- 电气工程 电气工程
- 机械工程 机械工程
- 数据科学数据科学数据科学
背景情况:
- 高压断路器对于电力系统的可靠性至关重要.
- 有限的故障数据和单一特征的限制阻碍了弹操作机制的有效诊断.
- 现有的方法难以捕捉静态弹疲劳特征.
研究的目的:
- 为高压断路器弹操作机制开发小样本故障诊断方法.
- 为了应对有限的故障样本和不足的特征表示的挑战.
- 提高故障诊断的准确性和可靠性.
主要方法:
- 构建了一个多源传感系统 (MEMS压力/行程,线圈/电机电流) 用于全面监控.
- 使用Savitzky-Golay过和物理特征提取创建了一个高维特征空间.
- 设计了一个双层堆叠集体学习模型,SVM,RF,KNN作为基础分类器,后勤回归作为meta-learner.
主要成果:
- 在正常和四种典型故障条件下达到96.1%的平均诊断准确率.
- 与单个基准模型相比,表现出优越的性能 (例如,RF在94.2%).
- 确定关闭和打开压力作为机械故障区分的关键特征.
结论:
- 拟议的多源功能融合和堆叠集体学习方法对于小样本故障诊断是有效的.
- 这种方法为基于条件的高压断路器维护提供了坚实的理论基础和技术支持.
- 非侵入性的MEMS传感器为捕获静态弹疲劳特征提供了解决方案.
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