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Deep learning-based fault diagnosis methods have to be trained by a large amount of labeled data for accurate diagnosis results. However, in real-case industrial scenarios, the data available is limited since the difficulty of obtaining fault samples. In addition, the monitoring data always come from various working conditions. As a result, data scarcity and domain shift have become two main factors that limit the performance of diagnosis models. We called this problem the few-shot cross-domain fault diagnosis, which has been rarely explored. To deal with the above challenge, we propose a novel uncertainty-based contrastive prototype-matching network (UCPMnet). Specifically, two main parts are conducted for UCPMnet. Considering the construction of representation spaces for compact intra-class and sparse inter-class embeddings, the contrastive prototypical network (CPN) is established by remolding the prototypical network. Subsequently, an uncertainty-based prototype-matching (UPM) method is presented for class-wise domain adaptation based on pseudo-label learning. At the same time, an estimation approach of prediction uncertainty is introduced to adaptively rectify the threshold for screening the pseudo label, which aims to mitigate the effect of noisy predictions for prototype-matching. Extensive experiments are conducted on two datasets, and four classical methods are selected for comparison. The results corroborate the superiority of the UCPMnet for fault diagnosis in the case of small data. [ABSTRACT FROM AUTHOR]

In image denoising, the recovery of high-frequency regions, such as image edges, directly affects the quality of the denoised images. However, previous deep learning-based denoising methods fail to effectively allocate the transmission of different frequency information and have difficulty giving network attention to high-frequency regions. In this paper, we rethink the fusion of image gradients in a neural network and deeply mine the intrinsic structure of the input image to propose a novel layered input gradient network (LIGN) for image denoising. The core of our network focuses on the features of different frequencies through two networks, which contain several key elements: (a) The input noise image is layered to widen the shallow layer of the network and to promote the hierarchical learning of different types of frequencies. (b) A multiscale feature extraction (MFE) block and information shunting (IS) block are proposed to integrate and separate various frequency features. (c) A gradient network (GradiNet) is designed to extract high-frequency information by network training, and the information is adaptively added to the input of the parallel main network (MainNet) through normalization to obtain high-quality images. Furthermore, we propose a sharpening loss function to enhance the texture details of the denoised image and improve visual quality. Extensive experiments on synthetic and real-world datasets show that the proposed method greatly enhances perceptual visual quality and achieves state-of-the-art performance on both PSNR and SSIM. The source code and pretrained models are available at https://github.com/JerryYann/LIGN. • A layered input gradient network (LIGN) based on a dual U-Net for high-quality image denoising is proposed. • Layered input and sharpening loss greatly improve the perceptual quality of the denoised image. • Multi-scale feature extraction block can capture more semantic information. • LIGN achieves the SoTA performance compared with the latest methods on synthetic and real noise datasets. [ABSTRACT FROM AUTHOR]