To address the limitations of traditional fault diagnosis methods for rotating machinery, which heavily rely on single-dimensional vibration data and fail to fully exploit the deep features of time-series data, this study proposes an innovative diagnostic model that integrates Gramian Angular Field-Parallel Convolutional Neural Network (GAF-PCNN) with Gated Recurrent Units (GRU). Specifically, one-dimensional vibration signals are first transformed into Gramian angular and difference fields as image representations using Gramian Angular Field (GAF). These two types of images are then input into parallel-configured PCNN modules for feature learning. The features extracted by the two CNN branches are weighted and fused to construct a combined feature sequence. This sequence is subsequently fed into the GRU network to capture temporal dependencies and perform deep feature extraction. In this process, an integrated self-attention mechanism is applied to dynamically select key features. The proposed method is validated using two publicly available datasets, including comparative and noise interference experiments. The results demonstrate that the proposed model excels in diagnostic accuracy, model generalization, and robustness against noise interference.

The fault diagnosis of industrial equipment is crucial for ensuring production safety and improving the efficiency of equipment operation. With the advancement of sensor technologies, the number of data generated in industrial environments has increased dramatically. Deep learning techniques, with their powerful feature extraction and classification capabilities, have become a research hotspot in the field of fault diagnosis. However, deep learning models are vulnerable to adversarial attacks, which can lead to a decrease in diagnostic accuracy and compromise system safety. This paper proposes a Joint Projection Gradient Descent (SCS-JPGD) method based on single-channel signal features. The proposed method first introduces a gradient-based attack approach for signal samples, which can add tiny perturbations to the input samples, causing misclassification in black-box models. Secondly, a joint training strategy is proposed for gradient attacks on signal samples, aiming to enhance the model’s adaptability to small perturbations in a limited range. Experiments were conducted on the CWRU dataset under four different operating conditions. The results show that, under a deep learning model with diagnostic accuracy exceeding 90%, the joint training method allows the model to maintain an average accuracy of 84.6% even after the addition of adversarial samples, which are barely distinguishable by the human eye. The proposed SCS-JPGD method provides a safer and more accurate approach for fault diagnosis in deep learning research.

Cloud coverage and obstruction significantly affect the usability of remote sensing images, making cloud detection a key prerequisite for optical remote sensing applications. In existing cloud detection methods, using U-shaped convolutional networks alone has limitations in modeling long-range contexts, while Vision Transformers fall short in capturing local spatial features. To address these issues, this study proposes a dual-branch framework, TransCNet, which combines Transformer and CNN architectures to enhance the accuracy and effectiveness of cloud detection. TransCNet addresses this by designing dual encoder branches: a Transformer branch capturing global dependencies and a CNN branch extracting local details. A novel feature aggregation module enables the complementary fusion of multi-level features from both branches at each encoder stage, enhanced by channel attention mechanisms. To mitigate feature dilution during decoding, aggregated features compensate for information loss from sampling operations. Evaluations on 38-Cloud, SPARCS, and a high-resolution Landsat-8 dataset demonstrate TransCNet’s competitive performance across metrics, effectively balancing global semantic understanding and local edge preservation for clearer cloud boundary detection. The approach resolves key limitations in existing cloud detection frameworks through synergistic multi-branch feature integration.