【摘要】 电气线路中因接触不良引起的发光连接故障极易诱发电气火灾，但发生发光连接故障的位置常常是未知的。为了解决检测困扰，搭建了发光连接试验平台，将检测重点聚焦为发光连接试验回路的回路电流，提取其时域、频域及时频域特征。针对实际场景中样本量少、数据不平衡问题，建立基于Wasserstein梯度惩罚生成对抗网络，用于数据增强。最后搭建一维卷积神经网络，对实验室得到的新样本进行识别。试验结果表明：发光连接故障与正常状态的回路电流在5～15 kHz特征频段可以进行区分。所搭建的发光连接故障识别模型训练准确率达99.93%，成功实现了实验室得到的15组回路状态的故障识别。更多还原

【Abstract】 Glowing contact faults in electrical circuits caused by poor connections are highly prone to triggering electrical fires, but the locations of glowing contact faults are often unknown. To solve the detection trouble, this paper builds a glowing contact experimental platform, focusing the detection on the loop current of the experimental circuit, and extracts its time-domain, frequency-domain, and time-frequency domain features. To address the problems of small sample size and data imbalance in actual scenarios, a Wasserstein Gradient Penalty Generative Adversarial Network is established for data augmentation. Finally, a one-dimensional convolutional neural network is built to identify new samples obtained in the laboratory. Experimental results show: glowing contact faults and normal loop currents can be distinguished in the characteristic frequency band of 5～15 kHz. The built glowing contact fault identification model achieved a training accuracy of 99.93%, successfully realizing fault identification for 15 sets of loop states obtained from the laboratory.更多还原