Object detection in remote sensing images is crucial in numerous fields; however, it becomes highly challenging under adverse weather circumstances. Given that previous remote sensing image object detection methods were designed based on normal weather conditions and ideal datasets, they are not beneficial for detection under real-world haze conditions and with class-imbalanced data. In this work, an adaptive dehazing centroid contrastive network (DHC-Net) is proposed to address the aforementioned issues. This network consists of an adaptive dehazing module and a centroid-guided contrastive learning approach. The adaptive dehazing module learns the image content to generate adaptive dehazing parameters, thus alleviating the influence of haze on the quality of remote sensing images. The centroid-guided contrastive learning approach is particularly designed to address the issue of imbalanced datasets. Integrating centroid vectors with actual samples in each training batch guarantees that each class is sampled at least once, effectively preventing the undersampling of minority classes. Moreover, dynamic weighted sampling based on prediction confidence guides the model to give priority to smaller classes, remarkably improving its ability to handle imbalanced data. Extensive experiments on the DOTA-v2.0, DOTA-v2.0Haze, RTTS, and HazeNet datasets demonstrate that DHC-Net is outstanding in handling haze conditions in remote sensing data, substantially enhancing target detection accuracy, even in the presence of imbalanced object classes. The source code will be available athttps://github.com/Linghuaqian1/DHC_Net

In this article, we propose a feature distillation-based uniformity few-shot domain adaptation (FUFD), for cross-domain fault diagnosis with sample shortage. To address the the few-shot problem, a uniformity prototypical contrastive network is designed to improve the data sensitivity of the model. Compared to the vanilla prototypical network, the learned prototypes contain more information about fault classes by encoding semantic structure information into the feature space while dynamically estimating the distribution concentration around each class prototype. Uniformity and correlation principles are introduced to alleviate prototype collapse: the uniformity principle ensures balanced prototype distribution, while the correlation principle enhances the diversity and distinctiveness of prototypical features. In addition, a cross-domain feature distillation-based domain adaptation module is designed to address significant domain shift. This module softens the class-specific information to capture more domain-consistent information and avoid overfitting to source working condition. Finally, experiments and ablation studies on cross-domain bearing fault diagnosis tasks with limited samples validate the effectiveness of FUFD and its individual modules in enhancing few-shot cross-domain fault diagnosis performance.

A certainty and transferability guided few-shot domain adaptation network is proposed to address few-shot open-set cross-domain fault diagnosis in this article. The proposed method is composed of a feature extractor, a certainty-guided prototypical contrastive module and a transferability weighting domain adaptation module. The certainty-guided prototypical contrastive module based on samples informative importance is designed to enhance the data sensitivity with limited samples while achieving well class separation for open-set scenarios. The module infers informative importance of samples to guide method learn more effective representations. Meanwhile, correlation and uniformity principles are incorporated to alleviate prototype collapse. The transferability weighting domain adaptation module is designed to address great domain gaps and negative transfer caused by asymmetrical label spaces. The module quantifies sample transferability and down-weights the irrelevant samples based on their transferability scores. Experimental results on few-shot open-set cross-domain bearing fault diagnosis tasks demonstrated the superior and effectiveness of the proposed method.

Unsupervised domain adaptation is widely used for fault diagnosis under variable working conditions. However, loss oscillation and slow convergence, which are caused by the dynamically varying alignment of targets during domain adaptation, are ignored. Therefore, a Gaussian mixture variational based transformer domain adaptation (GMVTDA) fault diagnosis method is proposed. A feature extractor based on transformer layers is designed to capture long-term dependency information and local features. Subsequently, a domain alignment term is proposed to project the features learned from both working conditions into the common assistance distribution and make them follow the same distribution after the alignment process. Additionally, considering that fault diagnosis is a multiclassification process, a Gaussian mixture is utilized to build the common assistance distribution. Ultimately, the proposed GMVTDA is applied to bearing fault diagnosis under variable working conditions, and the experimental results prove its effectiveness.

Accurate bearing fault diagnosis is of great significance of the safety and reliability of rotary mechanical system. In practice, the sample proportion between faulty data and healthy data in rotating mechanical system is imbalanced. Furthermore, there are commonalities between the bearing fault detection, classification, and identification tasks. Based on these observations, this article proposes a novel integrated multitasking intelligent bearing fault diagnosis scheme with the aid of representation learning under imbalanced sample condition, which realizes bearing fault detection, classification, and unknown fault identification. Specifically, in the unsupervised condition, a bearing fault detection approach based on modified denoising autoencoder (DAE) with self-attention mechanism for bottleneck layer (MDAE-SAMB) is proposed in the integrated scheme, which only uses the healthy data for training. The self-attention mechanism is introduced into the neurons in the bottleneck layer, which can assign different weights to the neurons in the bottleneck layer. Moreover, the transfer learning based on representation learning is proposed for few-shot fault classification. Only a few fault samples are used for offline training, and high-accuracy online bearing fault classification is achieved. Finally, according to the known fault data, the unknown bearing faults can be effectively identified. A bearing dataset generated by rotor dynamics experiment rig (RDER) and a public bearing dataset demonstrates the applicability of the proposed integrated fault diagnosis scheme.

The application of deep learning-based rolling bearing fault diagnosis methods in high reliability scenarios is limited due to low transparency. In addition, the scaling up of the deep learning models, in order to improve the performance of rolling bearing fault diagnosis (RBFD), has led to difficulties in its application in low-resource scenarios. Based on these facts, a new neural network, restricted sparse networks (RSNs), is proposed in this article. First, a restricted sparse frequency-domain space (RSFDS) is proposed for the interpretable representation of rolling bearing fault features (RBFFs) based on the quadratic complex domain equation. Second, an interpretable multichannel fusion mechanism is designed to map RBFFs to RSFDS. Furthermore, a high-power feature extraction module is developed to extract RBFFs in an efficient and easy-to-understand manner. Finally, an end-to-end RBFD network is provided for high reliability and resource-constrained scenarios. The experimental results show that RSNs have favorable fault diagnosis accuracy performance that is parallel to the state-of-the-art methods. More importantly, the model size of the proposed network only accounts for 20%–30% of the conventional methods.