高压断路器的机械故障定位和识别方法,基于振动信号的细分和混乱特征的提取
Shi Cao1, Tong Zhao1, Gang Wang2
1School of Electrical Engineering, Shandong University, Jinan 250061, China.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
本研究引入了一种新的方法,用于识别高压断路器 (HVCB) 的机械故障,使用振动信号分析. 该技术通过分析相位空间重建特征有效地定位故障,提高诊断准确度.
科学领域:
- 电气工程 电气工程
- 机械工程 机械工程
- 信号处理 信号处理
背景情况:
- 高压断路器 (HVCB) 的机械故障对电力系统可靠性构成重大风险.
- 准确及时识别这些缺陷对于防止故障至关重要.
- 现有的诊断方法可能缺乏复杂故障场景所需的精度.
研究的目的:
- 开发一种有效的方法来识别HVCBs中的机械故障.
- 提出一种基于振动子阶段的相空间重建的新型特征提取技术.
- 为了提高HVCBs故障诊断的准确性和本地化.
主要方法:
- 来自HVCBs的振动信号通过将其分为不同的子阶段来分析.
- 阶段空间重建应用于振动子阶段,以提取特征.
- 计算的关键特征包括最大的利亚普诺夫指数 (LLE),平均中心距离 (MCD) 和矢量直径偏移 (VDO).
- 对于每个子阶段,计算了自身值异常率,以精确确定缺陷位置.
- 一个支持矢量机 (SVM) 模型被训练用于故障诊断.
主要成果:
- 提出的方法在实验室条件下成功识别了HVCBs中常见的机械缺陷.
- 作为故障识别功能,LLE,MCD和VDO的组合被证明是有效的.
- 自值异常率准确地定位了有缺陷的振动子阶段.
- SVM模型在分类不同类型的机械故障方面表现出高准确度.
结论:
- 阶段空间重建方法为HVCBs的机械缺陷识别提供了强大的方法.
- 选择的特征 (LLE,MCD,VDO) 是故障条件的可靠指标.
- 这种技术提高了HVCB监控中精确故障定位和诊断的能力.
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