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一种基于超分辨率重建和逻辑推理的新型电气设备状态诊断方法

Peng Ping^1,2, Qida Yao², Wei Guo³

¹College of Aerospace Engineering, Chongqing University, Chongqing 400044, China.

Sensors (Basel, Switzerland)

|July 13, 2024

概括

本研究引入了一种用于准确电气设备故障诊断的新方法,该方法结合了用于降噪的生成对抗网络 (GAN) 和用于空间时间分析的深度学习,从而提高了电力系统的可靠性.

科学领域:

电气工程电气工程
人工智能的人工智能
电力系统电力系统

背景情况:

准确检测电气设备状态和故障对于电力系统可靠性至关重要.
深度学习视觉检查方法面临环境噪音和有限的逻辑推理能力的挑战.

研究的目的:

为电气设备开发可解释的故障诊断方法.
解决环境噪音问题,提高电力系统的局势意识.

主要方法:

使用生成对抗网络 (GAN) 来过噪音进行超分辨率图像重建.
深度学习分析像素信息以提取空间特征.
为电气设备集群构建逻辑图,以整合空间和时间状态.

主要成果:

拟议的方法有效地过了设备图像中的环境噪声.
空间特征和时间状态被整合为全面分析.
在6个数据集中证明了诊断电气设备故障的高准确性.

结论:

开发的可解释故障诊断方法显著提高了电气设备状态识别的准确性.
集成GAN和深度学习为复杂的电力系统监控和故障预测提供了强大的解决方案.

关键词:

电气设备状态诊断电气设备状态诊断图像超分辨率的超级分辨率逻辑推理逻辑推理时间信息融合,时间信息融合.

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