Softmax函数
计算机科学
人工智能
卷积神经网络
模式识别(心理学)
特征(语言学)
阈值
断层(地质)
特征提取
保险丝(电气)
噪音(视频)
降噪
频道(广播)
机制(生物学)
作者
Yingying Zhao,Yigang He,Zhikai Xing,Yongsheng Fu,Jianfei Chen,Bolun Du,Lei Wang
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:: 1-1
标识
DOI:10.1109/tim.2022.3203445
摘要
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.
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