基于时频图优化技术和ShuffleNetV2的真空接触器故障诊断方法
Haiying Li1, Qinyang Wang1, Jiancheng Song2
1School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Sensors (Basel, Switzerland)
|October 16, 2024
概括
本研究介绍了一种先进的真空接触器故障诊断方法,使用了通用的斯托克威尔变换 (GST) 和ShuffleNetV2.2. 该技术通过优化时间频率图表和增强特征提取,显著提高了诊断准确性和效率.
科学领域:
- 电气工程 电气工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 真空接触器在电力系统中至关重要,但由于功能提取效率低下和冗余数据,它们的故障诊断面临挑战.
- 现有的方法在诊断性能较低的情况下扎,需要改进的技术来实现可靠的操作.
研究的目的:
- 为真空接触器开发一种高性能故障诊断方法.
- 解决当前诊断方法中特征提取和数据冗余方面的局限性.
主要方法:
- 来自真空接触器的振动信号被转化为一般化的斯托克威尔变换 (GST) 时间频率图.
- 创建了多分辨率GST图形,并使用OTSU算法进行能量度面积裁剪,优化图形大小68.86%.
- 使用ShuffleNetV2网络来增强功能学习和故障分类.
主要成果:
- 拟议的方法为CKJ5-400/1140真空接触器实现了99.74%的故障识别精度.
- 模型训练的单次代时间减少了19.42%,表明效率有所提高.
- 优化的时间频率图和ShuffleNetV2集成有效地提高了诊断性能.
结论:
- 基于GST的时间频率图表优化与ShuffleNetV2相结合,为真空接触器提供了高度准确和高效的故障诊断解决方案.
- 这种方法有效地克服了不足的特征提取和数据冗余的局限性,为更可靠的电气设备监控铺平了道路.
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