In this study, an automatic test platform suitable for steering gears was established, which can test four sets of rudder systems separately. In addition, we propose an anomaly detection method based on deep learning technology to complete the automated multi-fault classification of the steering gear test. This paper combines the particle swarm optimization algorithm and the grey wolf optimization algorithm to optimize the convolutional neural networks (HPSOGWO-CNN). The proposed HPSOGWO-CNN model is constructed in two stages to realize the efficient and high-accuracy anomaly detection of the rudder system. In the first stage, through 10-fold cross validation, the optimal number of search agents of the HPSOGWO algorithm is obtained, and the performance is compared with GWO and PSO algorithms respectively. The results demonstrate that HPSOGWO algorithm is an excellent technique for automatic selection of hyper-parameters. In the second stage, the designed HPSOGWO algorithm is used to fine-tune the hyper-parameters of CNN, and a highly matched model for anomaly detection of rudder system test parameters was finally obtained. The experimental results show that the accuracy of this method is 99.846%, the precision is 99.748%, the recall is 99.498%, the F-score is 99.618%, and Kappa reaches 0.99565. CNN-based hybrid optimization for anomaly detection of rudder system, is advanced in comparison to KNN, SVM, BP, CNN, PSO-CNN, GWO-CNN, MGWO-CNN, WdGWO-CNN, RW-GWO-CNN models, in terms of accuracy, precision, recall, F-score, and kappa, respectively. Moreover, it is not affected by the imbalance samples, and can achieve accurate classification for small training samples.

Bearing fault diagnosis is crucial for mechanical equipment as it ensures the reliable and efficient operation of machinery. Transfer learning based on Maximum Mean Discrepancy has been widely studied due to its strong performance and interpretability in bearing fault diagnosis using unlabeled datasets. Current research mainly aims to align the distributions between source and target domains, improving model adaptability. However, bearings operate under complex conditions, making it difficult to distinguish fine-grained fault characteristics. Additionally, substantial differences in data distribution present significant challenges to applying transfer learning in fault diagnosis. This paper presents a novel fault diagnosis method for cross-domain applications guided by metric learning to facilitate dynamic distribution adaptation. Initially, we implement a Fine-Grained Feature Extraction Network, utilizing metric learning to improve the model’s capacity to identify specific fault characteristics. To address the indiscriminate adaptation of marginal and conditional distributions across domains, we employ relative entropy to determine dynamic weights. It provides a quantitative assessment of different distributions relative importance in various tasks. Experimental results on six cross-operational fine-grained transfer fault diagnosis tasks and six cross-machine transfer fault diagnosis tasks demonstrated that the proposed Fine-grained Domain Dynamic Adaptive Network outperforms typical domain adaptation methods, achieving an average accuracy of 98.79% and 86.07% respectively.

The rudder system is extensively used in aerospace, ships, missiles and other safety demanding areas. Therefore, it is paramount to ensure that the performance of the system is optimal. Rudder system testing equipment is a special tool used to diagnose its failure. Traditional ones can only artificially analyze the massive and complex tested data. Due to the low-test degree of automation, the performance of such testing tools is limited. Aiming to address this shortcoming, we developed a new rudder system testing equipment with four independent loading platforms and intelligent data analysis systems. It sufficiently shortens the installation and commission time of pneumatic actuators and the processing time of the testing data which largely improves its performance and accuracy. Given the imbalanced nature of the data an adaptive sampling algorithm considering informative instances (ASCIN) leveraging the Support Vector Machine (SVM) is proposed to process the originally collected data. The optimal parameters in SVM and ASCIN are searched by Whale Optimization Algorithm (WOA). Experiments are designed to assess the performance of ASCIN in comparison with existing approaches in the area of imbalanced data learning. The results show that the algorithm developed in this study has higher performance relative to traditional approaches. The application of these intelligent algorithms in fault detection and location of rudder system overcomes the limitation of traditional testing equipment and provides a new concept for future research into more intelligent one.

The object detection of natural images tends to obtain advanced semantic information through multiple convolutions and pooling, ignoring the detailed information in the feature map. Pixel-level images may be the target we are looking for in remote sensing images. This article designs a new attention mechanism that can fully utilize the spatial and channel information of the image, strengthen the region of interest, and try to protect the image's original information. Find the most influential location information in the spatial dimension and the most influential feature map in the channel dimension. Strengthen important channels and positions in the feature map to make vital information stronger and weak information not lost. Combine the designed attention mechanism with existing modules to enhance YOLO-V7 detection capability. We have merged two publicly available remote sensing image datasets, increasing object types, and richer appearance features, which can better detect model performance. Experimental results on an improved dataset have shown that the enhanced model in this paper can improve the detection ability of small and medium-sized targets in complex backgrounds, with a 1% increase in mean average precision (mAP) value and a maximum improvement of 8.2% for single-class targets. Medium targets such as airports, dams, and soccer ball fields also increase by about 5%. We also conducted experiments on the DOTA1.0 dataset to demonstrate that mAP improved by 1.1%, with 13 target categories having higher APs. The improved model reduces computational complexity by 2.7%, which is very user-friendly for embedded devices.

Fiber Optic Gyroscope (FOG) is a new type of rotation sensor that plays an important role in navigation and guidance system. As the sense element of FOG, the fiber coil (FOC) performance directly affects the FOG measurement precision. Many factors influence the quality of FOC, ideal structure and constant small tension control in the winding process were key factors. In order to prevent FOC edge defect, we proposed piecewise variable speed winding method to make regular FOC structure more accessible. In view of the shortage of the common control method used in FOC winding, we optimized fuzzy PID by introducing domain adjusting factors and designed a new tension controller based on mechanical analysis. The optimization controller can adaptively adjust the control factors to ideal settings and ensure constant tension throughout all variable winding speeds stages even in the present of complex external disturbance. Comparative experiments shown that the new controller has a higher accuracy and better robustness than commonly used controller.

Target detection from the aerial perspective of drones plays a crucial role in various fields. However, due to its unique high-altitude overhead view, images captured often exhibit a high proportion of small-sized targets amidst complex backgrounds and varying scales, posing significant challenges for detection. To address these issues, the EDR-YOLOv8 model has been proposed for drone-based aerial target detection. Firstly, the backbone of YOLOv8l is replaced with the high-resolution visual module EfficientViT, reducing the parameter count while maintaining the model’s capability to express important features. Secondly, the feature fusion network is redesigned with a four-level prediction layer to enhance the detection accuracy of small-sized targets. Additionally, the lightweight dynamic upsampler DySample is introduced to preserve more detailed target information. Finally, we design the feature fusion module C2f_RepGhost, which integrates the RepGhost bottleneck structure with YOLOv8’s C2f, thereby reducing computational complexity. Experimental results demonstrate that EDR-YOLOv8 achieves a 4.1% higher mAP@0.5 compared to the baseline YOLOv8l on the VisDrone2019-DET dataset, with a reduction of 40.5% in model size and 42.0% in parameter count. This illustrates that EDR-YOLOv8 achieves both lightweight modeling and improved detection accuracy.