基于轻型变压器的生成对抗网络用于转换器门的声学异常检测
Mingzhu Tang1, Chen Yin1, Haijun Hu2
1School of Energy and Power Engineering, Changsha University of Science & Technology, Changsha, 410114, China.
ISA transactions
|November 13, 2025
概括
本研究介绍了一种基于轻型变压器的生成对抗网络 (LT-GAN),用于使用声学分析在高压直流转换器中无监督检测异常. 新的框架实现了卓越的性能和效率,解决了数据不平衡和资源有限等挑战.
科学领域:
- 电气工程 电气工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 在高压直流 (HVDC) 转换器中检测异常的声学分析至关重要,但面临着挑战.
- 不常见的异常,计算限制和类不平衡阻碍了现有方法的实际部署.
研究的目的:
- 提出一种新的无监督异常检测框架,即基于轻型变压器的生成对抗网络 (LT-GAN),用于HVDC转换器.
- 解决声学异常检测中的数据不平衡和资源限制问题.
主要方法:
- 开发了LT-GAN,其中包含MobileNet V2和D-MobileNet V2用于Mel频谱处理.
- 整合了一个K-ViT块,以改善全球代表性学习和减少网络参数.
- 在现实世界声学数据集上利用无监督学习.
主要成果:
- LT-GAN实现了0.9806的曲线下的面积 (AUC),显著超过了基线方法.
- 在低假阳性区域 (0.9295,0.9122,0.9559) 达到高的部分AUC (p-AUC).
- 证明了特殊的轻量级特性:9.626M参数,0.506 GFLOPs,37.48 MB的模型大小.
结论:
- 拟议的LT-GAN有效地使用声学数据检测HVDC转换器中的异常.
- 对于实际部署,LT-GAN提供了一个资源高效的解决方案,平衡高检测性能与低计算开销.
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