Ping Peng, Yao Qida, Guo Wei, Liao Changrong
College of Aerospace Engineering, Chongqing University, Chongqing 400044, China.
School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China.
Sensors (Basel). 2024 Jun 30;24(13):4259. doi: 10.3390/s24134259.
The accurate detection of electrical equipment states and faults is crucial for the reliable operation of such equipment and for maintaining the health of the overall power system. The state of power equipment can be effectively monitored through deep learning-based visual inspection methods, which provide essential information for diagnosing and predicting equipment failures. However, there are significant challenges: on the one hand, electrical equipment typically operates in complex environments, thus resulting in captured images that contain environmental noise, which significantly reduces the accuracy of state recognition based on visual perception. This, in turn, affects the comprehensiveness of the power system's situational awareness. On the other hand, visual perception is limited to obtaining the appearance characteristics of the equipment. The lack of logical reasoning makes it difficult for purely visual analysis to conduct a deeper analysis and diagnosis of the complex equipment state. Therefore, to address these two issues, we first designed an image super-resolution reconstruction method based on the Generative Adversarial Network (GAN) to filter environmental noise. Then, the pixel information is analyzed using a deep learning-based method to obtain the spatial feature of the equipment. Finally, by constructing the logic diagram for electrical equipment clusters, we propose an interpretable fault diagnosis method that integrates the spatial features and temporal states of the electrical equipment. To verify the effectiveness of the proposed algorithm, extensive experiments are conducted on six datasets. The results demonstrate that the proposed method can achieve high accuracy in diagnosing electrical equipment faults.
准确检测电气设备的状态和故障对于此类设备的可靠运行以及维持整个电力系统的健康状况至关重要。通过基于深度学习的视觉检测方法，可以有效地监测电力设备的状态，这些方法为诊断和预测设备故障提供了重要信息。然而，存在重大挑战：一方面，电气设备通常在复杂环境中运行，从而导致捕获的图像包含环境噪声，这显著降低了基于视觉感知的状态识别准确性。这进而影响了电力系统态势感知的全面性。另一方面，视觉感知仅限于获取设备的外观特征。缺乏逻辑推理使得单纯的视觉分析难以对复杂的设备状态进行更深入的分析和诊断。因此，为了解决这两个问题，我们首先设计了一种基于生成对抗网络（GAN）的图像超分辨率重建方法来过滤环境噪声。然后，使用基于深度学习的方法分析像素信息以获取设备的空间特征。最后，通过构建电气设备集群的逻辑图，我们提出了一种可解释的故障诊断方法，该方法整合了电气设备的空间特征和时间状态。为了验证所提算法的有效性，在六个数据集上进行了广泛的实验。结果表明，所提方法在诊断电气设备故障方面能够实现高精度。
