在振动条件下基于N-M-M-B的系列弧度故障检测
Yanli Liu1, Ganqing Yang1, Huiyang Wang1
1Department of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China.
这项研究提出了一种用于检测经历机械振动的电机中的连续弧故障的新方法. 该方法有效地识别故障,甚至可以区分电机故障类型,确保可靠的电源供应.
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:
- 电机中的机械故障会产生振动,这可能导致电气接触器的连续弧故障.
- 连续的电弧故障会损害电源的可靠性.
- 在振动下检测这些故障对于系统完整性至关重要.
研究的目的:
- 开发和验证一种方法来检测不同机械振动条件下的连续弧断.
- 为了识别导致振动和随后的电弧故障的特定电机故障类型.
主要方法:
- 在受控的振动条件下生成串行弧断,收集故障相位电流和弧电压信号.
- 利用噪声辅助多变量实证模式分解和对应系数用于故障特征信号选择.
- 采用混合深度学习模型 (多尺度卷积神经网络和双向长短期记忆网络) 进行故障检测和分类.
主要成果:
- 拟议的方法可以有效地检测振动下的连续弧断.
- 该模型可以初步识别导致机械振动的电机故障类型.
- 开发的模型表现出强大的噪声免疫力和良好的概括能力.
结论:
- 开发的系列弧故障检测方法是有效和可靠的,即使在机械振动的存在下也是如此.
- 该系统提供了故障检测和初步故障源识别的双重功能.
- 该方法在确保动态电机环境中的电源供应可靠性方面取得了重大进展.
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