对具有有限样本的模拟电路的故障诊断策略,基于变压器和发电模型的组合
Zhen Jia1, Qiqi Yang1, Yang Li2
1School of Mechanical and Electrical Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China.
Sensors (Basel, Switzerland)
|November 25, 2023
概括
本研究介绍了一种新的故障诊断方法,用于使用深卷积生成对抗网络 (DCGAN) 和变压器架构的模拟电路. 该方法有效地解决了有限的故障样本,即使使用最小的数据,也可以达到超过96%的准确性.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 模拟电路在电子系统中至关重要,但由于缺陷样本有限,故障诊断具有挑战性.
- 现有的诊断方法在对模拟电路故障检测的数据不足方面扎.
研究的目的:
- 为模拟电路提出一种创新的故障诊断方法.
- 为了克服在诊断性能中缺陷样本不足的局限性.
主要方法:
- 将深层卷积生成对抗网络 (DCGAN) 与变压器架构集成.
- 使用连续波段转换与Morlet波段用于时间频率图像生成.
- 通过DCGANs产生的合成时间频率信号来增强数据集.
主要成果:
- 拟议的方法在各种单一和多重故障场景中实现了超过96%的整体准确性.
- 即使只有150个真实故障数据样本,也表现出令人满意的性能.
- 在比较分析中表现优于既定基准方法.
结论:
- 结合的DCGAN和变压器方法有效地提高了对有限数据的模拟电路故障诊断.
- 该方法为需要高诊断精度的现实应用提供了强大的解决方案.
- 时间频率图像分析和数据增强是提高诊断性能的关键.
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