用热成像与卷积自编码器实现电力转换电路故障100%精准检测
Fault Detection Method for Power Conversion Circuits Using Thermal Image and Convolutional Autoencoder
- 通过热图像训练自编码器，仅对正常样本学习重建
- 在多种工况下故障检测准确率达100%
- 适合电力电子设备维护人员参考
提出一种基于热成像与卷积自编码器的电力转换电路故障检测方法。自编码器在不同负载电流下采集的商用功率模块热图上进行训练，并通过缩放、旋转、透视变换及明暗对比调整等图像增强技术生成增强图像。由于自编码器仅对正常样本学习完全重建，即使输入包含故障的图像，也能输出近似正常图像。在电路板上附加小型加热器模拟故障，从不同角度、位置及负载电流下采集热图像以测试模型性能。采用曲线下面积（AUC）评估方法，结果表明在给定条件下，该模型可实现100%的异常检测准确率。同时研究了卷积层数量和图像增强条件等超参数对检测精度的影响。
原文摘要 · Abstract (English)
A fault detection method for power conversion circuits using thermal images and a convolutional autoencoder is presented. The autoencoder is trained on thermal images captured from a commercial power module at randomly varied load currents and augmented image2 generated through image processing techniques such as resizing, rotation, perspective transformation, and bright and contrast adjustment. Since the autoencoder is trained to output images identical to input only for normal samples, it reconstructs images similar to normal ones even when the input images containing faults. A small heater is attached to the circuit board to simulate a fault on a power module, and then thermal images were captured from different angles and positions, as well as various load currents to test the trained autoencoder model. The areas under the curve (AUC) were obtained to evaluate the proposed method. The results show the autoencoder model can detect anomalies with 100% accuracy under given conditions. The influence of hyperparameters such as the number of convolutional layers and image augmentation conditions on anomaly detection accuracy was also investigated.
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