Hongfu Zuo

Reliable online condition monitoring is essential for maintaining process stability and guaranteeing machining quality in precision grinding. Against this background, this study proposes an electrostatic induction-based measurement strategy and further performs systematic comparisons with conventional force-based monitoring methods. First, an electrostatic sensor model is established to quantitatively characterize charge transfer behaviors during grinding interactions. A three-axis experimental grinding platform is deployed to correlate electrostatic responses and mechanical force signals with critical grinding variables, including grinding speed, depth of cut, feed rate, and wheel wear severity. To achieve quantitative and objective evaluation, a unit-free Dynamic Sensitivity Change Degree (DSCD) index is introduced. Comparative results based on the DSCD index reveal that electrostatic signals exhibit better performance than traditional force-based signals in tracking grinding wheel speed variations and progressive wear evolution under the tested conditions. Meanwhile, the DSCD fluctuation in electrostatic signals remains within 1% under varying cutting depths, indicating favorable linear stability within the scope of the present experiments. Furthermore, a Generalized Conditional Variational Auto-Encoder (G-CVAE) model is developed to augment insufficient wheel wear datasets. The generated synthetic signals exhibit high fidelity and enable accurate and robust classification of grinding wheel wear states. This study verifies the existence of explicit quantitative correlations between electrostatic induction signals and key grinding parameters as well as wheel wear conditions. The proposed monitoring method can provide high-quality, reliable data support for subsequent grinding condition assessment and intelligent process decision-making.

This study introduces an all-fiber optic sensing network based on fiber Bragg grating (FBG) technology for structural health monitoring (SHM) of launch vehicle payload fairings under extreme thermo-mechanical conditions. A wavelength–space dual-multiplexing architecture enables full-field strain and temperature monitoring with minimal sensor deployment. Structural deformations are reconstructed from local measurements using the inverse finite element method (iFEM), achieving sub-millimeter accuracy. High-temperature experiments verified that FBG sensors maintain a strain accuracy of 0.8 με at 500 °C, significantly outperforming conventional sensors. Under 15 MPa mechanical loading and 420 °C thermal shock, the fairing structure exhibited no damage propagation. The sensing system captured real-time strain distributions and deformation profiles, confirming its suitability for aerospace SHM. The combined use of iFEM and FBG enables high-fidelity large-scale deformation reconstruction, offering a reliable solution for reusable aerospace structures operating in harsh environments.

This study introduces an all-fiber optic sensing network based on fiber Bragg grating (FBG) technology for structural health monitoring (SHM) of launch vehicle payload fairings un-der extreme thermo-mechanical conditions. A wavelength–space dual-multiplexing ar-chitecture enables full-field strain and temperature monitoring with minimal sensor de-ployment. Structural deformations are reconstructed from local measurements using the inverse finite element method (iFEM), achieving sub-millimeter accuracy. High-temperature experiments verified that FBG sensors maintain a strain accuracy of 0.8 με at 500 °C, significantly outperforming conventional sensors. Under 15 MPa mechanical loading and 420 °C thermal shock, the fairing structure exhibited no damage propagation. The sensing system captured real-time strain distributions and deformation profiles, con-firming its suitability for aerospace SHM. The combined use of iFEM and FBG enables high-fidelity, large-scale deformation reconstruction, offering a reliable solution for reusa-ble aerospace structures operating in harsh environments.

Low-energy impacts have been demonstrated to cause damage and failure in aircraft structures, thereby affecting the structural load-bearing performance and creating safety hazards. In this study, an innovative damage-monitoring method based on a fiber Bragg grating (FBG) is proposed for honeycomb sandwich composites. The proposed method is applicable to honeycomb sandwich composites and integrates a light gradient boosting machine (LightGBM)-optimized impact localization method with feature-parallel and data-parallel processing in the machine learning architecture. An impact localization algorithm is applied to honeycomb sandwich composites using an array of multiplexed FBG sensors. The proposed algorithm exhibited substantial localization accuracy. The LightGBM method was employed to identify the optimal branching points for impact localization in real time, addressing the low-accuracy challenge in localizing low-energy impacts on the board structure when the fiber grating sensing system operates at a high sampling frequency.

Low-energy impacts have been demonstrated to cause damage and failure in aircraft structures, thereby affecting the structural load-bearing performance and creating safety hazards. In this study, an innovative damage-monitoring method based on a fiber Bragg grating (FBG) is proposed for honeycomb sandwich composites. The proposed method is applicable to honeycomb sandwich composites and integrates a light gradient boosting machine (LightGBM)-optimized impact localization method with feature-parallel and data-parallel processing in the machine-learning architecture. An impact localization algorithm is applied to honeycomb sandwich composites using an array of multiplexed FBG sensors. The proposed algorithm exhibited substantial localization accuracy. The LightGBM method was employed to identify the optimal branching points for impact localization in real time, addressing the low-accuracy challenge in localizing low-energy impacts on the board structure when the fiber grating sensing system operates at a high sampling frequency.

Foreign object debris (FOD) in civil aviation environments poses severe risks to flight safety. Conventional detection primarily relies on manual visual inspection, which is inefficient, susceptible to fatigue-related errors, and carries a high risk of missed detections. Therefore, there is an urgent need to develop an efficient and convenient intelligent method for detecting aircraft FOD. This study proposes a detection model based on a Siamese network architecture integrated with a spatial transformation module. The proposed model identifies FOD by comparing the registered features of evidence-retention images with their corresponding normally distributed features. A dedicated aircraft FOD dataset was constructed for evaluation, and extensive experiments were conducted. The results indicate that the proposed model achieves an average improvement of 0.1365 in image-level AUC (Area Under the Curve) and 0.0834 in pixel-level AUC compared to the Patch Distribution Modeling (PaDiM) method. Additionally, the effects of the spatial transformation module and training dataset on detection performance were systematically investigated, confirming the robustness of the model and providing guidance for parameter selection in practical deployment. Overall, this research introduces a novel and effective approach for intelligent aircraft FOD detection, offering both methodological innovation and practical applicability.

Checking the distance between aircraft fire detection lines is a crucial task in the conformity inspection process of civil aircraft manufacturing. Currently, this task is mainly performed manually, which is inefficient and prone to errors and omissions. To address this issue, we propose a method for detecting the distance between aircraft fire detection lines based on the Segment Anything Model (SAM). In this method, we develop a general model for aircraft parts image segmentation and detection, named the Aircraft Segment Anything Model (ASAM). This model uses a low-rank fine-tuning strategy (LoRA) to fine-tune the encoder and mask decoder synchronously and incorporates a fusion loss function for adaptive training of the target task. The trained ASAM model is tested on a self-built fire detection line segmentation dataset, achieving Dice scores of 85.28, 85.99, and 86.26 based on sam_vit_b, sam_vit_l, and sam_vit_h weights, respectively. It scores 13.82 points higher than the classic segmentation model K-Net under the same training parameters. The proposed method provides a new approach for the widespread application of SAM in the field of aircraft parts image segmentation.

The presence of water accumulation on aircraft surfaces constitutes a considerable hazard to both performance and safety, necessitating vigilant inspection and maintenance protocols. In this study, we introduce an innovative semantic segmentation model, grounded in deep learning principles, for the precise identification and delineation of water accumulation areas within infrared images of aircraft exteriors. Our proposed model harnesses the robust features of ResNet, serving as the foundational architecture for U-Net, thereby augmenting the model’s capacity for comprehensive feature characterization. The incorporation of channel attention mechanisms, spatial attention mechanisms, and depthwise separable convolution further refines the network structure, contributing to enhanced segmentation performance. Through rigorous experimentation, our model surpasses existing benchmarks, yielding a commendable 22.44% reduction in computational effort and a substantial 38.89% reduction in parameter count. The model’s outstanding performance is particularly noteworthy, registering a 92.67% mean intersection over union and a 97.97% mean pixel accuracy. The hallmark of our innovation lies in the model’s efficacy in the precise detection and segmentation of water accumulation areas on aircraft skin. Beyond this, our approach holds promise for addressing analogous challenges in aviation and related domains. The enumeration of specific quantitative outcomes underscores the superior efficacy of our model, rendering it a compelling solution for precise detection and segmentation tasks. The demonstrated reductions in computational effort and parameter count underscore the model’s efficiency, fortifying its relevance in broader contexts.

In response to the challenge of mitigating flight delays, this study introduces an innovative solution that encompasses the prediction of delay durations for existing flights and the subsequent optimization of ground service processes based on these predictions. The indirect forecasting of flight delays is achieved through the construction of a random forest model, exhibiting a remarkable 100% accuracy when considering a 15-minute standard for flight delays. In light of the delay prediction outcomes, distinct delay coefficients are assigned to individual flights, facilitating the development of a ground service optimization model through the application of a genetic algorithm. Within the genetic algorithm optimization framework, significant enhancements have been implemented in the gene encoding of the initial population, incorporating a segmented encoding approach. Employing this refined model to optimize the service sequence and duration of ground service vehicles for all flights culminates in the notable accomplishment of achieving zero delays for the entire set of flights.