Partial domain adaptation (PDA) for fault identification has been widely researched to help construct self-monitoring systems in the era of the Industrial Internet of Things (IIoT). However, the existing PDA fault identification methods neglect the influence of uncertainty of the target domain on the identification performance. To solve this problem, this work developed a prototype-guided partial domain adaptation method with momentum weight for fault diagnosis. Specifically, to reduce the risk of ruling out the outlier by the output of a classifier or a discriminator, a class-wise selectively source weighting strategy that follows the number of the target pseudo labels is proposed. The target instances’ pseudo labels, which are obtained by calculating the distance between the target instance and the source prototypes, are irrelevant to the classifier and discriminator. Further, the momentum algorithm, by which the historical weights information could be retained, is employed in the source weights calculation procedure to alleviate the fluctuation and more closely to the global optimal. Experiments demonstrated the effectiveness and superiority of the developed method.

Deep learning, as a good way of learning discriminative features automatically from original data, has been widely used for fault diagnosis in machinery. However, the excellent performance of most deep learning algorithms relies heavily on the characteristics of data for training, including but not limited to a rich set of labelled samples and class-balanced dataset, which are not typical cases in practical applications due to factors such as tagging cost, fault pattern (typical pattern and atypical pattern of machine fault). In this paper, a contrastive learning-based fault diagnosis method was developed for rotating machinery with limited and class-imbalanced samples. An improved dual-view wavelet feature attention fusion embedded contrastive network is designed to enhance the diagnostic knowledge extraction capability of the contrastive learning network and also properly make good use of the available fault information contained in the large number of unlabeled datasets. Furthermore, a dynamic weighted loss is designed by matching datasets of various class cases adaptively and dynamically to alleviate the impacts of class imbalance. Compared with other methods with which to diagnose planetary gearbox fault, the experimental results show that the method proposed achieves higher diagnostic accuracy under conditions of limited and imbalanced labels.

In recent years, deep learning-based fault diagnosis methods have witnessed significant advancements and successful applications in engineering practice. However, the increasing complexity of network structures demands higher computational resources in terms of floating point operations per second (FLOPs) and parameters, which poses challenges when deploying diagnostic models in edge computing scenarios with limited run resources. To address this issue, this study proposes a novel lightweight network, namely cheap ghost network (CGhostNet), incorporating fine-grained feature knowledge distillation (FFKD). FFKD-CGhostNet leverages CGhostNet, a lightweight architecture, and transfers diagnostic knowledge from ResNet, a complex yet high-performing network, through FFKD. Extensive experiments are conducted on two test benches to demonstrate that FFKD-CGhostNet achieves comparable diagnostic performance to ResNet while significantly reducing parameter count by nearly 88 times and computational requirements by almost 14 times. These findings highlight the effectiveness of FFKD-CGhostNet in achieving superior diagnostic performance in resource-constrained edge computing scenarios.

Image Quality Assessment (IQA) plays an important role in the field of computer vision. However, most of the existing metrics for Blind IQA (BIQA) adopt an end-to-end way and do not adequately simulate the process of human subjective evaluation, which limits further improvements in model performance. In the process of perception, people first give a preliminary impression of the distortion type and relative quality of the images, and then give a specific quality score under the influence of the interaction of the two. Although some methods have attempted to explore the effects of distortion type and relative quality, the relationship between them has been neglected. In this paper, we propose a BIQA with coarse-grained perception construction and fine-grained interaction learning, called PINet for short. The fundamental idea is to learn from the two-stage human perceptual process. Specifically, in the pre-training stage, the backbone initially processes a pair of synthetic distorted images with pseudo-subjective scores, and the multi-scale feature extraction module integrates the deep information and delivers it to the coarse-grained perception construction module, which performs the distortion discrimination and the quality ranking. In the fine-tuning stage, we propose a fine-grained interactive learning module to interact with the two pieces of information to further improve the performance of the proposed PINet. The experimental results prove that the proposed PINet not only achieves competing performances on synthetic distortion datasets but also performs better on authentic distortion datasets.