Multibranch 1-D CNN Based on Attention Mechanism for the DAB Converter Fault Diagnosis

Abstract

This article considers the fault diagnosis problem for the dual active bridge (DAB) converter. Given that information from multiple diagnostic signals needs to be combined effectively for fault diagnosis of the DAB converter and diagnostic signals have noise, a multi-branch 1-D convolutional neural network (CNN) based on attention mechanism is proposed. First, the proposed network processes multiple raw 1-D diagnostic signals without extracting features manually. Then, soft thresholding and attention mechanism are combined to acquire denoising thresholds, which adaptively removes channel noise from feature maps. Besides, the attention mechanism is utilized to adaptively obtain importance weights of the feature maps learned by different 1-D CNN branches, which helps to learn the most useful features and fuse the multi-branch features effectively. Finally, the diagnostic results are obtained by inputting the fused features into the Softmax layer. The experimental results indicate that the proposed method with denoise module and feature fusion module improves the diagnostic accuracy effectively. Contrastive experiments show that the proposed method is superior to other fault diagnosis methods.