基于两轴振动和多传感器融合的变压器绕组机械故障诊断

针对传统变压器绕组机械故障诊断方法中，仅考虑绕组单一方向振动信号且特征参数提取复杂、识别准确率低的问题。本文提出了一种基于两轴振动和多传感器融合的变压器绕组机械故障诊断方法。首先从绕组轴向、辐向振动相关性角度提出两轴振动关系图形作为特征图像；然后采用轻量级卷积神经网络MobileNet V2对不同传感器获得的图像数据进行训练；最后利用D-S证据理论对多维信息源识别结果进行融合，并做出最终决策。实验结果表明所提方法故障诊断准确率可达99.4%,与传统故障诊断方法相比，简化特征提取步骤，诊断准确率提高了6.2%以上，为变压器绕组机械故障诊断提供一种可行方案。

Transformer winding mechanical fault diagnosis based on two-axis vibration and multi-sensor fusion

In the traditional transformer winding mechanical fault diagnosis method, only the winding axial vibration is considered, and the feature parameter extraction is complex and the recognition accuracy is low. This paper presents a mechanical fault diagnosis method for transformer windings based on two-axis vibration and multi-sensor fusion. Firstly, the two-axis vibration relationship graph is proposed as the feature image from the perspective of the axial and radial vibration correlation of the winding. Then the lightweight convolutional neural network MobileNet V2 is used to train the image data obtained by different sensors. Finally, the D-S evidence theory is used to fuse the multi-dimensional information source recognition results and make the final decision. The experimental results show that the fault diagnosis accuracy of the proposed method can reach 99.4%. Compared with the traditional fault classification method, the feature extraction step is simplified, and the diagnostic accuracy is improved by more than 6.2%, which provides a feasible scheme for mechanical fault diagnosis of transformer winding.