Introduction: The increasing emphasis on environmental sustainability and lightweight development has made fiber winding technology an essential automated production method for producing composite structures. High-performance fiber-wound composite products are essential catalysts for the growth of industries including aerospace, automotive, energy, and chemicals, significantly contributing to the advancement of high-end manufacturing. Methods: This study examines the ideas and techniques of fiber winding technology, encompassing the processes involved in pipe winding. We assess the current state of technology by conducting a thorough analysis of patents related to fiber winding machines, categorized into three groups based on their attributes: equipment for winding rotary bodies, equipment for winding shaped bodies, and components for winding machines. Results: This study reveals several notable achievements in the field, including enhancements in winding processes, improvements in winding quality and efficiency, and the creation of numerous mechanisms and devices designed to optimize the operation of winding equipment. Nevertheless, obstacles remain, such as the need for increased precision and stability in tension regulation, expanded adaptation to complex geometries, and enhanced automation and intelligence in winding machinery. Discussion: The results indicate a clear trend toward automation, intelligence, and high precision in fiber winding technology. The integration of digital twin technology, robotics, and advanced control systems is seen as a promising direction for future development. These advancements could significantly improve the flexibility, adaptability, and intelligence of fiber winding machines, addressing current limitations and meeting the growing market demands for high-performance composite products. Conclusion: This study offers insights into technological advancements and innovation trends in fiber winding machines. It underscores the necessity for additional research and development to address current technological hurdles and advance fiber winding technology. The future of fiber winding technology hinges on its ability to adapt to complex working conditions, optimize process parameters in real-time, and achieve intelligent, efficient, and sustainable manufacturing of fiber-wound pipes.

Although traditional homogeneous SiCp reinforced aluminum matrix composites have characteristics such as high specific strength, low thermal expansion coefficient, and excellent wear resistance, but their isotropic properties are difficult to meet the gradient requirements of material properties under complex working conditions. Therefore, SiCp reinforced aluminum matrix composite gradient materials have attracted much attention in aerospace, defense and military industries. This study focuses on the preparation of SiCp/6092Al composite materials with different silicon carbide contents (15%, 20%, and 25%) using the powder metallurgy method. Gradient composite materials were prepared using the ECAP method, and the resulting variations in their microstructure and properties were systematically analyzed. The results indicate that: Based on powder metallurgy technology and large plastic deformation ECAP technology, SiCp/6092Al gradient composite materials with good interfacial bonding have been prepared, and the results of hardness and tensile strength tests show that compared with single volume fraction materials, the prepared gradient composite material has the characteristics of surface ablation resistance, intermediate layer high thermal conductivity, and matrix toughening.

This article proposes a new method for bearing fault diagnosis based on sparse representation classification to address the challenges of fault identification under complex working conditions with different degrees of damage. The core of this method lies in directly using the original vibration signal to construct an overcomplete dictionary without the need for signal denoising or manual feature extraction in advance, thus avoiding the information loss and subjective bias introduced by denoising and feature engineering in traditional methods. Firstly, all training samples are used as a dictionary to sequentially solve for sparse coefficients for each test sample. Secondly, the corresponding parts of each category in the sparse coefficients are filtered out. Then, the category error is calculated based on the sparse coefficients corresponding to each category. Finally, the fault classification of bearings is carried out by comparing the category errors. The experimental results show that this method can maintain high diagnostic accuracy and robustness in complex scenarios with various working conditions and damage levels, verifying its effectiveness and universality for bearing fault diagnosis.

Abstract Stray light is one of the significant factors influencing the target detection capability. During actual observation, the level of stray light is not only associated with the stray light suppression capacity of the space telescope itself, but also closely related to the operating conditions in orbit. Being aware of the current level of stray light is conducive to evaluating the image quality of the captured images, which is a necessary step for conducting scientific image simulation.
In order to rapidly estimate the stray light levels under actual complex working conditions, this paper investigates a set of stray light analytical methods for China Space Station Telescope (CSST). The developed model is capable of simulating the stray light background generated by various light sources such as moonlight, starlight, and earthshine outside the field of view under different conditions. It also accounts for zodiacal light background as well as the scattering and ghost images caused by bright stars inside field of view. This model offers a relatively accurate and rapid assessment of stray light levels in actual operational conditions, providing an important complement to image simulation and offering necessary guidance for observation scheduling.

This article takes the shield tunnelling project of the southern entrance of Line 7 of Changsha Metro as a practical background, focusing on extremely complex working conditions including shallow cover (with a minimum thickness of only 3.8m, equivalent to 0.62 times the shield diameter D), small curve radius (R only 250m), low strength rock strata (unconfined compressive strength ranging from 2.25 to 5.96MPa), and the cutting line reception process.The conventional shield tunneling method typically faces challenges in controlling surface settlement and increased risks of segment misalignment and deviational errors under extreme conditions such as shallow overburden and tight curvature radii. In this context, a systematic and in-depth study of the key technologies during the shield reception phase was conducted. Through optimized earth pressure balance control, synchronous grouting process, dynamic regulation of the articulated system, and the application of precise guidance technology, combined with numerical simulation and real-time monitoring methods. Ultimately, the surface settlement was controlled within 12.5mm, segment misalignment to ≤10.0mm, shield posture deviation within ±25mm, and reception portal seal leakage to ≤0.05L/min. These results provide valuable technical references for shield reception construction in shallow cover with small radii, and have significant guiding implications for similar projects.

With the acceleration of high-performance, green, and intelligent agricultural equipment, premature wear and failure of agricultural machinery tools became a key bottleneck that restricted the high-quality development of agricultural machinery and equipment. Digital twin technology provided innovative theoretical and technical support, which enabled the accurate prediction and evaluation of the wear performance of agricultural machinery tools under dynamic and complex working conditions. This paper explained the key elements of digital twin technology and summarized the development history of tool wear research, categorizing it into three stages: physical experiment-driven, numerical simulation, and digital twin integration. Additionally, it highlighted the progress made in agricultural machinery tools based on digital twin technology, particularly in data acquisition, modeling, and data-driven approaches. The paper also introduced a case study of a self-developed agricultural machinery tool wear performance test machine. However, it addressed the key challenges faced in the application of digital twin technology for monitoring agricultural machinery tool wear, including difficulties in data perception and fusion, insufficient accuracy in multi-physical field modeling, and inadequate real-time performance. Future research focused on developing accurate multi-physics field coupling models, optimizing data processing mechanisms, and creating intelligent analysis frameworks. Additionally, it aimed to promote low-cost and efficient digital twin solutions to enhance the intelligence level and feasibility of agricultural machinery tool wear monitoring.

In rail damage detection, the scale variation of small targets leads to inaccurate extraction of damage morphology and size features, thereby affecting the reliable identification of damage types. The DETR algorithm has been optimized and improved. Firstly, we introduce the convolution–attention fusion module (CAFMAttention) after the two side convolutional layers of the original algorithm; then, we replace the nn.Upsample-based upsampling layer with the Dysample upsampler. Finally, we replace the Conv modules in the two down-sampled convolutional layers with Dual-Conv modules. The results of the comparative experiments show that the recall rate of the improved DETR model in this paper is 0.698, which is 12.2% higher than that of the original DETR model. The accuracy is 0.815, which is 2.3% higher than that of the original DETR model. The average precision (Map@0.5) is 0.741. Compared with the original DETR model, it has been improved by 8.7%. The F1 score is 0.75, which is 8.7% higher than the original DETR model. The frame per second (FPS) transfer rate is 64.94, which is 2.6% higher than that of the original DETR model. The proposed DETR algorithm can detect rail damage under complex working conditions well, with high accuracy and robustness, and better meet the requirements of practical actual rail detection.

Abstract In the process of utilising machine vision-assisted large aircraft component docking assembly, due to the occlusion induced by process equipment such as assembly tooling, the features on the calibration board cannot be extracted by each camera at the same time, resulting in calibration difficulties or calibration failure. This paper aims to propose a stereo calibration method for multi-cameras in large aircraft component assembly to improve calibration accuracy. Firstly, the sub-pixel edge extraction method based on Canny-Zernike is proposed to accurately extract the circular edges and circle centres of the calibration board, and the Zernike moment model is improved. The circle centre sorting method based on the triangular markers is introduced to realise the sorting of circle centres on the calibration board. Secondly, the intrinsic and extrinsic parameter models of multi-cameras and the visual parameter models between cameras are constructed, and Zhang’s calibration method and indirect calibration method are integrated to solve the parameters. Subsequently, the distortion correction model is optimised by Levenberg-Marquardt. Finally, experiments are performed to test the proposed method. The results show that the proposed method, compared with uncalibration and Zhang’s calibration method, the proposed method achieves stereo calibration of the multi-cameras under complex working conditions, enhances the calibration accuracy and improves the quality of the large aircraft component docking assembly.

ABSTRACT As the core structural material for light‐weighting in aerospace and other engineering fields, the actual service performance of composites is often subject to extreme temperatures and complex working conditions. Considering the effects of the service temperature of commercial aircraft from −54°C to 127°C and the matrix glass transition temperature (143°C), this paper systematically investigates the mechanical response of carbon‐fiber‐reinforced polyether‐ether‐ketone (CF/PEEK) composites over a wide range of temperatures (−60°C to 300°C). The results show that the bearing capacity of fibers and matrix is different under different loads, and the matrix‐dominated conditions (shear) are more sensitive to temperature changes, and the strength decay is significantly faster than that of the fiber‐dominated conditions (tension and compression). However, when the ambient temperature > , the compressive strength decay rate increases dramatically due to the weakening of the lateral support of the matrix to the fibers under compression. Post‐experimental specimen fracture observations indicate that the failure mode of CF/PEEK tends to be brittle at low temperatures. As the temperature increases, the CF/PEEK failure mode tends to be tougher due to the degradation of matrix mechanical properties and the weakening of the fiber‐matrix interface bonding. is the critical temperature for the transition of the CF/PEEK failure mode at high temperatures. Therefore, temperature protection should be emphasized for CF/PEEK structures under compression/shear loading, and special attention needs to be paid to the risk of steep performance degradation due to the phase transition of the matrix near .

Under complex working conditions, traditional fault detection methods have limitations like many parameters and complex calculations. To solve this, a bearing fault detection model based on smooth dilated convolution and shuffling algorithm was proposed. It uses smooth convolution kernels to capture local vibration-signal features, reduces computational complexity via group convolution and channel washing, simplifies the structure with network pruning and knowledge distillation, and combines bidirectional gated recurrent units and generative adversarial networks to capture long-term dependencies. Compared with existing methods, it significantly cuts the number of model parameters and reasoning time while keeping detection accuracy. Experimental data shows that in the sample classification task, its accuracy rate is 97.88%, average reasoning time is 274 fps, computational cost is 1.66 FLOPs, and parameter quantity is 7.76M, all better than comparison models. In bearing feature extraction and fault detection tasks, its average fitting accuracy is 96.13% and detection accuracy is 99.62%, also better than comparison models. The research suggests the model can balance model lightweighting and detection performance, and is suitable for real-time fault monitoring in resource-constrained scenarios.

With the rapid development of industries such as construction and port hoisting, the operational safety of truck cranes in crowded areas has become a critical issue. Under complex working conditions, traditional monitoring methods are often plagued by issues such as compromised image quality, increased parallax computation errors, delayed fence response times, and inadequate accuracy in dynamic target recognition. To address these challenges, this study proposes a personnel intrusion detection system based on multimodal sensor fusion and dynamic prediction. The system utilizes the combined application of a binocular camera and a lidar, integrates the spatiotemporal attention mechanism and an improved LSTM network to predict the movement trajectory of the crane boom in real time, and generates a dynamic 3D fence with an advance margin. It classifies intrusion risks by matching the spatiotemporal prediction of pedestrian trajectories with the fence boundaries, and finally generates early warning information. The experimental results show that this method can significantly improve the detection accuracy of personnel intrusion under complex environments such as rain, fog, and strong light. This system provides a feasible solution for the safety monitoring of truck crane operations and significantly enhances operational safety.

Rolling bearings are critical components in rotating machinery and play a vital role in industrial production. However, due to complex working conditions and high operational loads, the risk of bearing failure increases significantly. Accurately predicting the remaining useful life (RUL) of rolling bearings is essential for enhancing the reliability and safety of industrial equipment. To address the challenges in accurately determination of the First Prediction Time (FPT) under weak early-stage faults and the insufficient utilization of temporal information in existing methods, this study proposes the following improvements: First, the data preprocessing procedure is optimized by introducing a frequency-energy ratio-based method for FPT determination, enabling precise identification of incipient weak faults. Second, a synthetic time-frequency graph is innovatively constructed to comprehensively capture the evolutionary trends of time-frequency features. Finally, a parallel deep learning architecture is designed to separately extract temporal and feature information from the bearing degradation process, followed by deep fusion for accurate RUL prediction. Experimental validation conducted on the XJTU-SY bearing dataset demonstrates that the proposed parallel model significantly enhances the accuracy of life prediction.

Thin-seam shearers operating in complex coal seams work under adverse conditions with poor visibility, making sensor installation difficult and signal sensing and collection challenging. As a result, identifying the cutting state becomes difficult, which significantly impacts the intelligent control of the shearer’s cutting section. Additionally, the complex working conditions lead to low reliability and shorten the service life of the spiral drum. The spiral drum is a typical symmetrical structure, and its load exhibits both symmetry and nonlinearity. The load under different gangue-inclusion conditions is developed in MATLAB R2022a. The occurrence times and corresponding load-spectrum data of the spiral drum, both under natural wear and sudden impact conditions, are extracted. Analysis reveals that the maximum stress under natural wear conditions exceeds 300 MPa, while under sudden impact conditions it reaches over 600 MPa. Fatigue analysis is carried out with the help of the ANSYS Ncode 2022 R1 module to identify the weak positions of fatigue damage in the spiral drum structure. Reliability models for natural wear and sudden impact failures are established using the Gamma and Weibull distributions, respectively. Parameter estimation is performed, and competing failure reliability models are constructed under independent and correlated conditions of the two failure modes. This approach obtains the competing reliability curve of the spiral drum, providing data support and new ideas for its reliability design.

ABSTRACT The coal-gangue sorting robot is of great significance to the intelligent development of coal mines, and coal-gangue detection technology is the core technology of the robot. To address the issues of low recognition efficiency and insufficient accuracy in traditional detection technologies, the AHM-YOLO model is proposed to solve the above issues. This research focuses on the application of the AHM-YOLO model in coal and gangue object detection, the model incorporates the C2f-AKConv module in the backbone network to provide flexible convolution for enhancing feature extraction. The neck network features the Hybrid Feature Enhancement Module (HFEM) that captures global and local details through multi-scale attention feature fusion. The Multi-Task Convolutional Detection Head (MCDH) in the head network optimizes the detection process by decoupling tasks and introducing an IoU prediction branch. Experimental results on a self-made dataset demonstrate the effectiveness of these improvements. In the ablation experiment, compared to the baseline YOLOv8s, adding the modules successively significantly boosts Precision, Recall, mAP50, mAP50-95, and F1 score. In comparison with other common models, AHM-YOLO model achieves better detection performance. Moreover, the model maintains stable recognition performance under complex working conditions such as noise, motion blur and low light, and has generalization capabilities for public datasets. Therefore, the AHM-YOLO model can provide reliable technical support for efficient identification of the coal-gangue sorting robots.

This paper addresses the issue of fault diagnosis in high-speed train bogie bearings under complex working conditions and proposes a method for calculating the characteristic frequency of rolling bearings that takes into account the influence of radial clearance. By establishing a five-degree-of-freedom nonlinear dynamic model, this study systematically analyzes the modulation mechanism of radial clearance on the fault characteristic frequency of bearings and verifies the findings through an experimental platform. The results indicate that an increase in clearance not only leads to significant attenuation of the fault characteristic frequency amplitude, but also induces sideband modulation effects, thereby interfering with fault diagnosis accuracy. The experimental data show good agreement with the theoretical calculations, verifying the effectiveness of the proposed method. Specifically, the nonlinear stiffness-based characteristic frequency calculation reduces the prediction error from 6.9–5.7% under traditional theory to 2.3–3.4% across a wide range of rotational speeds. Meanwhile, the clearance-induced amplitude attenuation predicted by the model is also experimentally confirmed, with measured amplitude reductions of 35–42% as clearance increases from 0.2 μm to 0.5 μm. These results not only demonstrate the accuracy and engineering applicability of the method but also provide new theoretical foundations and practical references for health monitoring and early fault diagnosis of high-speed train bearings.

Abstract —Tool wear detection in mechanical machining is a critical link for ensuring product quality and improving production efficiency. However, this field faces challenges such as scarce annotated data and interference from complex working conditions, making it difficult to deploy advanced detection models. To address the fundamental mismatch between model capacity and data availability, this paper proposes a novel data-efficient hybrid detection architecture named MD-YOLOV12. This architecture ingeniously integrates the rich general visual representations learned by the self-supervised vision transformer model DINOv3 with the YOLOv12 object detection framework. Specifically, we perform feature enhancement at two key locations: input preprocessing and the middle layer of the backbone network, thereby enhancing the model's perception and recognition capability for tool wear features without relying on massive annotated data. To validate the method's effectiveness, we constructed a specialized tool wear detection dataset containing 8083 high-resolution images, meticulously annotated into three categories: "No Wear," "Moderate Wear," and "Severe Wear." Extensive experimental results demonstrate that the proposed MD-YOLOV12 method surpasses existing state-of-the-art techniques in the tool wear detection task, providing a viable technical pathway for data-efficient industrial vision applications.

A bearing intelligent fault diagnosis method based on an improved convolutional neural network is proposed to address the problems of high noise, difficult fault feature extraction, and low fault diagnosis recognition rate in rolling bearing vibration signals collected under complex working conditions. Firstly, in the data preprocessing stage, the wavelet denoising method is used to preprocess the data to obtain higher-quality signals. Then, the convolutional neural network LeNet-5 model was improved through batch normalization, Dropout, and L2 regularization methods. The wavelet denoised signal was input into the optimized LeNet-5 model to achieve more accurate fault diagnosis output for rolling bearings. Finally, to demonstrate the generalization ability of the model, this paper uses publicly available rolling bearing data from a university as the dataset and conducts experimental verification of the model using MATLAB-2023b software under different loads. The experimental results show that the improved neural network model has a fault diagnosis accuracy of 94.27%%, which is 17.84% higher than the traditional neural network model in terms of accuracy. Moreover, for different loads, the improved convolutional neural network model still maintains good fault diagnosis accuracy.

Under complex working conditions, multidirectional vibration and impact seriously restrict the stable operation and service life of engineering structures and precision equipment. Traditional cushioning and vibration isolation systems focus on single‐direction nonzero stiffness design, which is difficult to meet the needs of multidimensional impact and vibration coupling conditions. For this reason, this work draws inspiration from efficient energy‐absorbing structures in nature and proposes an integrated bionic quasi‐zero‐stiffness (QZS) structure design strategy that incorporates cuttlefish bone‐mimicking S‐shaped structure with a beetle shell‐mimicking center ring cross structure. It is shown that the S‐shaped cubic cross center ring‐cross structure (S‐CCCRCS) developed through this design strategy exhibits a distinct QZS plateau and achieves outstanding energy absorption performance in the X , Y , and Z directions. Under impact loading, the S‐CCCRCS structure shows excellent cushioning performance with the lowest peak impact loads in all three directions; at the same time, due to its three‐dimensional QZS, the structure achieves highly efficient vibration isolation in the multidirectional low‐frequency range. The bionic composite QZS structure proposed in this work provides a feasible solution for high‐performance, multidirectional cushioning and vibration isolation in the fields such as rehabilitation medicine and smart devices.

This research examines the work environment, challenges, and coping mechanisms of elementary school teachers as they implement school-designed autonomous time, using Lipsky’s Street-Level Bureaucracy model. Eight elementary school teachers were selected for individual in-depth interviews, and the interview data were analyzed using a comparative analysis method. According to the findings, the teachers faced complex working conditions, including ambiguous policy intentions, chronic shortages of resources, and involuntary collaboration with colleagues. In response to such issues, two distinct patterns of adaptation emerged. One was a passive adaptation type, which involved reducing or simplifying the operation of the autonomous time. The other was an active adaptation type, where teachers utilized the difficult work environment as an opportunity for collaboration with colleagues and for individual and communal growth. It was analyzed that these differences in adaptation patterns stemmed from differences in perception regarding teacher professionalism, the curriculum, and the overall school community. Based on these findings, the research suggests establishing a practice-centered training system to support teachers as curriculum developers, to create an institutional foundation that respects and protects teachers' practical knowledge and educational decisions, and to institutionalize a democratic deliberation culture within schools.

Abstract Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is crucial for ensuring the safe and reliable operation of battery systems. However, due to the highly nonlinear degradation behavior of lithium-ion batteries under complex working conditions, traditional prediction methods often fail to achieve satisfactory accuracy. This study proposes a hybrid prediction model that integrates an attention (ATT) mechanism, a convolutional neural network (CNN), and a bidirectional long short-term memory (BiLSTM) network, termed ATT-CNN-BiLSTM. First, a one-dimensional CNN is employed to extract local degradation patterns and inter-channel correlated features from multi-dimensional sensor time-series data. Then, the BiLSTM network captures long-term dependencies within the degradation process. Furthermore, an attention mechanism is introduced to dynamically adjust the contribution of key time-step features and suppress noise interference. Experimental results based on the NASA battery aging datasets demonstrate that the proposed ATT-CNN-BiLSTM model reduces RMSE and MAE by 49.08% and 54.67%, respectively, compared with the LSTM model, and by 10.89% and 17.85%, respectively, compared with the ACNN-LSTM-MMD model. Ablation experiments also confirm that further confirm that the ATT-CNN-BiLSTM model decreases RMSE and MAE by 26.45% and 33.33%, respectively, compared with CNN-BiLSTM, and by 21.85% and 23.36%, respectively, compared with ATT-BiLSTM.