基于多模态信息融合的变压器在线故障诊断方法

Transformer online fault diagnosis method based on multi-modal information fusion

针对变压器的多模态数据中存在差异性和样本缺失的问题,提出了一种基于振动信号和红外图像数据的多模态信息融合方法,分析多模态数据对电力变压器故障状态进行有效、快速的评估。首先,该方法采用双向门控神经网络对振动信号的文本信息、振动信号的频域图和变压器的红外图像分别进行特征提取,并获得不同模态的重要特征向量。然后,使用交叉注意力机制建立不同模态之间的联系并进行特征向量融合。最后,通过卷积层和全链接层输出电力变压器的故障状态。实验数据采集于10 kV变压器,含振动信号和变压器的红外图像。实验结果表明,提出的多模态信息融合方法在4种评价指标上优于对比方法,其故障诊断准确率为96%。在不同的电压和电流等级下多模态信息融合方法能获得较为可靠的诊断结果且准确率高,可为变压器多模态数据的故障检测提供方法。

Addressing the challenges posed by variability and missing samples in multimodal data,we introduce a Multi-modal Information Fusion(MIF)technique that leverages both vibration and infrared image data.This innovative approach facilitates an effective and rapid assessment of power transformer fault states.First,a bidirectional gated recurrent unit(BGRU)is employed to extract features from the textual data of vibrations,the frequency images derived from vibrations,and the infrared images captured from the power transformer.The BGRU then yields feature vectors corresponding to different modalities.Subsequently,a cross-attention mechanism is utilized to establish relationships between these diverse modalities,enabling feature vector fusion.Finally,a combination of convolutional and fully connected layers determines the fault status of the power transformer.The experiment data come from the 10 kV power transformer,which contains the vibration signal and infrared images.Comparative analysis reveals that the MIF method outperforms benchmark techniques across four evaluation metrics,achieving a commendable fault diagnosis accuracy rate of 96%.Furthermore,the MIF methodology demonstrates its robustness by delivering highly reliable diagnostic outcomes under varying voltage and current conditions,offering a promising solution for the detection of faults in transformer multimodal data.