Deep learning algorithms have become prevalent in equipment Prognostics and Health Management (PHM) modeling, yet their deployment in safety-critical applications remains constrained by inherent epistemic uncertainty. This study advances uncertainty calibration methodologies for two fundamental PHM tasks: classification and time-series prediction. First, we proposed a regularization-free calibration framework that dynamically adjust labels to achieve accuracy-uncertainty alignment in classification models. Building on this, we presented the first comprehensive uncertainty calibration framework for time-series prediction models, along with pioneering evaluation metrics specifically designed for temporal uncertainty assessment. To validate our methodology, we acquired comprehensive fault and degradation datasets from aircraft actuators. The evaluation framework encompassed two critical dimensions: (1) classification calibration across four distinct neural architectures on three benchmark datasets, and (2) degradation calibration evaluated across three neural architectures utilizing eight publicly available datasets. Our experimental results consistently demonstrated statistically significant enhancements in calibration performance metrics. The complete implementation is publicly available at https://github.com/ppqweasd/uncertainty-calibration.

Quantifying predictive uncertainty is crucial for building trustworthy decision-making systems in signal processing. However, many existing embedded evaluation methods rely on prior assumptions about the distributions of network parameters, which can compromise model accuracy. To address this, we proposed a distribution-aware uncertainty quantification framework for Bayesian neural networks. Our method explicitly accommodates different parameter distributions, thereby reconciling model fidelity with reliability. Theoretically, distribution-specific update rules through variational inference were derived. As a practical enhancement, we introduce an efficient fine-tuning strategy that initializes Gaussian distribution means through deterministic pre-training, significantly accelerating computation while maintaining competitive uncertainty estimation quality. Experimental results on aerospace oxygen concentrators and standard benchmarks demonstrate that our method maintains high prediction fidelity while providing reliable uncertainty estimates. The implementation is publicly available at: https://github.com/ppqweasd/Uncertainty-Quantification.

Aircraft fault knowledge graphs serve as a critical knowledge base for the intelligent maintenance and operations of aviation equipment. However, the entity alignment tasks in their construction remain overly dependent on manual annotation, leading to issues such as inconsistent annotation quality and low annotation efficiency. Unsupervised methods provide a promising solution and have garnered significant research interest. However, existing unsupervised entity alignment approaches often overlook the impact of noisy entities, presenting a significant challenge for aligning entities in aviation fault data. This paper proposes a solution by incorporating a large language model (LLM) into the entity alignment process for aircraft fault knowledge graphs. By leveraging the world knowledge encoded in the LLM, the approach enhances the performance of unsupervised entity alignment models. Specifically, we introduce the Collaboratively Enhanced-based Large Language Model Entity Alignment (CELLMEA), which utilizes data from the aircraft flight control system manual, fault analysis manual, and typical fault cases. The model's architecture includes a multi-view semantic information embedding that integrates structural, relational, and semantic data. Additionally, we propose an adaptive method for mixing hard negative samples, which generates higher-quality negative entities by combining noisy negative samples with reliable ones. Furthermore, an incremental consistency regularization technique is introduced to progressively refine the robustness of pseudo-labeling within the CELLMEA model. Finally, experimental results on a flight control system entity alignment dataset demonstrate that CELLMEA outperforms all baseline models, achieving an MRR (Mean Reciprocal Rank) value of 0.917 +/- 0.011. These results validate the model's effectiveness in handling unlabeled data and lay the groundwork for the engineering of aircraft fault knowledge graphs.

Aiming at the problem that the efficiency of the LLC converter in a single-phase hybrid inverter is reduced due to the excessive losses of the high-frequency transformer, this paper proposes a copper foil-copper wire composite winding scheme. In this scheme, multiple strands of parallel-wound copper wires and copper foils are alternately wound, and the large current on the secondary side is allowed to flow through the copper foil winding to reduce the losses. Firstly, this paper conducts theoretical designs for the two winding schemes. Secondly, simulation modeling is carried out in Ansys Maxwell. Finally, the effectiveness and feasibility of the proposed scheme are verified on a 6 kW off-grid and grid-connected inverter experimental platform.

Addressing the reliance on manual annotation in current aircraft failure knowledge graph modeling, which results in inconsistent annotation quality and low efficiency, we propose semi-supervised methods as a potential solution paradigm for this problem, which have attracted extensive research interest. However, existing work on semi-supervised relational extraction ignores the effect of data imbalance on semi-supervised relational extraction, posing a challenge for semi-supervised relational extraction in aviation fault data. To address this challenge, this paper introduces a semi-supervised relation extraction method. This method improves the performance of the relation extraction model by utilizing unlabeled data. Specifically, we propose an adaptive meta-learning semi-supervised relation extraction model utilizing aircraft flight control system manuals, fault analysis manuals, and typical fault cases as data sources. Firstly, the pseudo-label generation network and the relationship classification network are established separately, and the relationship classification network is used as a meta-objective to optimize the pseudo-label generation network by using meta-learning. Next, the adaptive thresholding method combining global thresholding and local thresholding is used to optimize the selection of pseudo-labels. Furthermore, the incremental learning approach is introduced to improve the robustness of the model. Finally, taking the flight control system fault dataset as an example, the experimental results show that the model's performance is higher than all the baseline models and achieves an F1 score of 77.803 +/- 0.614% with 5% labeled data, which verifies the validity of the model in the case of insufficient labeled data and lays a foundation for the engineering of the knowledge graph of aircraft faults. The code for AdaMetaSRE is available at https://github.com/lijh01/AdaMetaSRE.

The ports of the bi-directional converter exhibit negative impedance characteristics when the energy storage unit of a DC microgrid is operating in charging mode. This can decrease the system’s stability margin, potentially leading to oscillation instability. To address this issue, we propose a feed-forward compensation control method based on impedance shaping. This involves designing a transfer function within the current feed-forward loop of the energy storage converter to ensure that the current reference value follows changes in the bus voltage. Selecting an appropriate time constant ensures that the port impedance of the energy storage unit exhibits a positive resistive characteristic close to the oscillation frequency while retaining a negative impedance characteristic in the low-frequency range. This ensures the stability of system operation. MATLAB/Simulink simulations and RT-LAB semi-physical platform testing have verified that the proposed control method effectively enhances the system’s stability margin, ensuring stable operation and minimal steady-state voltage deviation.

The increasing deployment of photovoltaic (PV) systems necessitates robust fault detection mechanisms to ensure operational reliability and safety. Conventional approaches, however, struggle in complex industrial environments characterized by high noise, data incompleteness, and class imbalance. This study proposes an innovative Advanced CNN-BiLSTM architecture integrating multi-scale feature extraction with hierarchical attention to enhance PV fault detection. The proposed framework employs four parallel CNN branches with kernel sizes of 3, 7, 15, and 31 to capture temporal patterns across various time scales. These features are then integrated by an adaptive feature fusion network that utilizes multi-head attention. A two-layer bidirectional LSTM with temporal attention mechanism processes the fused features for final classification. Comprehensive evaluation on the GPVS-Faults dataset using a progressive difficulty validation framework demonstrates exceptional performance improvements. Under extreme industrial conditions, the proposed method achieves 83.25% accuracy, representing a substantial 119.48% relative improvement over baseline CNN-BiLSTM (37.93%). Ablation studies reveal that the multi-scale CNN contributes 28.0% of the total performance improvement, while adaptive feature fusion accounts for 22.0%. Furthermore, the proposed method demonstrates superior robustness under severe noise (σ = 0.20), high levels of missing data (15%), and significant outlier contamination (8%). These characteristics make the architecture highly suitable for real-world industrial deployment and establish a new paradigm for temporal feature fusion in renewable energy fault detection.

This study proposes an enhanced photovoltaic panel defect detection method based on an optimized YOLOv8 model, addressing common defects like scratches, broken grids, and dirt. To improve feature extraction for small or blurry defects, the C2f-DySnakeConv module is added to the backbone network. The traditional upsampling process is replaced by the WaveletPool module, which uses wavelet transforms to enhance multi-scale features and improve feature resolution. A lightweight EfficientHead detection head is also introduced to boost the model’s accuracy and robustness in complex detection scenarios. Additionally, to combat sample imbalance and improve the classification of challenging defects, a dynamic weight-adjusted Focal Loss function is employed. Experimental results show that the proposed CWE-YOLOv8 model achieves an mAP of 87.9%, a 5.1% improvement over the YOLOv8n baseline. Despite these advancements, the model retains a compact size of 7.26MB, only slightly larger than the original model’s 5.94MB. This approach offers a high-precision, real-time, and lightweight solution for PV defect detection, with notable potential for engineering applications.

In this paper, a fast recognition algorithm based on Deep Convolutional Neural Network (DCNN) is proposed and studied for the speed and accuracy of infrared target recognition in missile terminal guidance system. This algorithm makes full use of the advantages of deep learning model in feature extraction and pattern recognition, combined with the unique characteristics of infrared imaging, and designs and trains a deep convolutional neural network model suitable for the infrared target recognition task of missile terminal guidance. By optimizing network structure, adopting advanced training strategy and fusing multi-scale and multi-view infrared image information, the constructed model can realize efficient and accurate recognition of infrared targets under complex background, and meet the real-time requirements. The experimental results show that the algorithm not only significantly improves the accuracy of target recognition, but also ensures the ability to process a large amount of infrared image data in real time under the condition of high-speed flight, so as to effectively improve the strike accuracy and combat efficiency of the missile terminal guidance system, which has important theoretical value and practical application significance for promoting the development of precision guidance weapon technology in China.

To tackle the problems of over-reliance on traditional experience, poor troubleshooting robustness, and slow response by maintenance personnel to changes in faults in the current aircraft health management field, this paper proposes the use of a knowledge graph. The knowledge graph represents troubleshooting in a new way. The aim of the knowledge graph is to improve the correlation between fault data by representing experience. The data source for this study consists of the flight control system manual and typical fault cases of a specific aircraft type. A knowledge graph construction approach is proposed to construct a fault knowledge graph for aircraft health management. Firstly, the data are classified using the ERNIE model-based method. Then, a joint entity relationship extraction model based on ERNIE-BiLSTM-CRF-TreeBiLSTM is introduced to improve entity relationship extraction accuracy and reduce the semantic complexity of the text from a linguistic perspective. Additionally, a knowledge graph platform for aircraft health management is developed. The platform includes modules for text classification, knowledge extraction, knowledge auditing, a Q&A system, and graph visualization. These modules improve the management of aircraft health data and provide a foundation for rapid knowledge graph construction and knowledge graph-based fault diagnosis.

Air gap discharge is one of the most basic scientific problems in the field of high-voltage engineering. The homogeneous electric field 1.5 mm air gap negative streamer at overvoltage and atmospheric pressure is observed by a high-speed 4-channel framing camera. The ultra-high temporal resolution images of a single negative stream are captured (the exposure time is 5 ns, and the inter-frame delay is no more than 0.1 ns). It is observed that the negative streamer formed in the middle of the air gap and grew bidirectionally towards both electrodes. At the same time, the electrical measurement is also carried out.

This paper discusses in detail the design and implementation of the terminal guidance system based on the composite tracking strategy of fusion template matching and Harris corner detection technology. Through the complementary advantages and deep integration of these two mature visual tracking methods, the aim is to improve the target recognition accuracy, tracking stability and anti-interference ability of the missile in the complex environment, so as to optimize the strike efficiency in the final guidance stage. Firstly, the basic principles of template matching and Harris corner detection are analyzed in detail. Then, the paper describes how to organically merge the two to form a compound tracking strategy. Specifically, template matching is used to quickly lock the approximate location of the target, and its keen ability to capture the significant features of the target is used to improve the success rate of first capture and the speed of tracking initialization. Then, Harris corner detection is introduced to extract and describe fine features of the target, and a robust set of target feature points is constructed to effectively deal with complex conditions such as target deformation, rotation and occlusion. On this basis, an adaptive weight allocation mechanism is designed to dynamically adjust the contribution of template matching and corner detection according to the complexity and SNR of the current tracking environment, so as to ensure the optimal tracking performance under various working conditions. In the experimental part, the performance of the proposed composite tracking strategy is evaluated comprehensively with the help of simulation software and actual flight test data. The results show that compared with the single template matching or corner detection methods, the fusion strategy has a significant improvement in target recognition rate, tracking accuracy, tracking stability and anti-interference ability. In summary, this paper proposes a composite tracking strategy that integrates template matching and Harris corner detection, and successfully applies it to the missile terminal guidance system to achieve effective target identification and stable tracking, and significantly improve the strike efficiency and environmental adaptability of the missile during the terminal guidance stage. It provides a new idea and practical reference for the development of missile precision guidance technology.

Coupling faults that simultaneously occur during the operation of mechanical equipment are widespread. These faults encompass a diverse range of high-order coupling relationships, involving multiple base fault types. Based on the advantages of hypergraphs for higher-order relationship descriptions, two coupling fault diagnosis architectures based on the hypergraph neural network are proposed in this paper: 1. In the coupling fault diagnosis framework based on feature generation, the base faults serve as the hypergraph nodes, and each hyperedge connects the base faults. The generator, which consists of the hypergraph neural network, generates coupling faults as negative samples to enforce regularization constraints for the discriminator training. 2. In the coupling fault diagnosis framework based on feature extraction, each node represents a fault mode, and each hyperedge connects nodes with common failure modes. The multi-head attention mechanism extracts the features of base faults, and the common fault features in a hyperedge are aggregated via the hypergraph neural network. The inner product correlation is used to diagnose the fault modes. The results show that the diagnostic accuracy for coupling faults with the two frameworks reaches 88.6% and 86.76%, respectively. Both frameworks can be used for the diagnosis and analysis of high-order coupling faults.

This paper mainly discusses how to effectively use multi-core embedded digital signal processor (DSP) technology to realize parallel computation of image tracking algorithm and memory optimization in high precision missile terminal guidance system. With the increasing requirement of missile strike accuracy in modern war, the vision-based terminal guidance technology has been widely concerned because of its strong autonomy and anti-jamming ability. However, such image processing algorithms are usually highly computation-intensive and data dependent, which requires high real-time and hardware resources. In this paper, the key steps of image tracking algorithm are analyzed and reconstructed deeply, and the architecture suitable for parallel processing of multi-core DSP is designed by modularization. The parallel computing advantages of multi-core DSP are given full play through task division and scheduling strategies to improve the execution efficiency and real-time performance of the algorithm. Secondly, the research also focuses on memory optimization, including the design of data cache strategy, reducing data redundancy, improving memory access locality, etc., and strives to maximize the data reading speed and reduce the memory bandwidth pressure under the condition of limited embedded system resources. To sum up, this research is committed to building an efficient and stable multi-core embedded DSP environment, parallel computing model and memory optimization scheme for high-precision missile terminal guidance image tracking algorithm, which has important theoretical value and practical significance for improving the intelligence level and combat efficiency of missile terminal guidance system.

As the demand for enhanced maritime safety continues to grow, ship target detection technology has become increasingly crucial in the domain of ocean surveillance. Conventional target detection methods face challenges such as high computational demands and reduced detection efficiency in ship identification scenarios. This study introduces an optimized lightweight ship detection algorithm, building upon the YOLOv5 framework. By incorporating streamlined model architecture and parameter tuning, the model retains YOLOv5’s high accuracy while minimizing computational overhead and boosting detection speed. The proposed model integrates deep separable convolution and pruning techniques, effectively shrinking the network size without compromising detection precision. For system validation, a publicly available dataset featuring a variety of ships is employed in the experimental phase. The simulation outcomes indicate that the refined model sustains high detection accuracy while substantially decreasing computational load. Experimental findings reveal that the model achieves a mean Average Precision (mAP) of 92.3% and accelerates detection to 45 frames per second (FPS), marking a 15% improvement over the original YOLOv5 algorithm. This research presents a novel approach for ship detection in maritime monitoring, offering significant practical application potential.

Mechanical equipment is composed of several parts, and the interaction between parts exists throughout the whole life cycle, leading to the widespread phenomenon of fault coupling. The diagnosis of independent faults cannot meet the requirements of the health management of mechanical equipment under actual working conditions. In this paper, the dynamic vertex interpretable graph neural network (DIGNN) is proposed to solve the problem of coupling fault diagnosis, in which dynamic vertices are defined in the data topology. First, in the date preprocessing phase, wavelet transform is utilized to make input features interpretable and reduce the uncertainty of model training. In the fault topology, edge connections are made between nodes according to the fault coupling information, and edge connections are established between dynamic nodes and all other nodes. Second the data topology with dynamic vertices is used in the training phase and in the testing phase, the time series data are only fed into dynamic vertices for classification and analysis, which makes it possible to realize coupling fault diagnosis in an industrial production environment. The features extracted in different layers of DIGNN interpret how the model works. The method proposed in this paper can realize the accurate diagnosis of independent faults in the dataset with an accuracy of 100%, and can effectively judge the coupling mode of coupling faults with a comprehensive accuracy of 88.3%.

针对寿命预测模型迁移问题,提出了一种长短周期记忆网络微调(long short-term memory fine tune,LSTM-fine-tune)的迁移模型,利用理想条件下的试验数据对模型进行训练.在迁移过程中,对部分LSTM网络层进行冻结,利用实际服役环境下的数据对网络其他部分进行修正.为验证模型的泛化能力,采用不同相位与幅值的正弦函数生成数据,通过学习数据获取正弦函数的经验知识,并应用至其他正弦函数的回归,结果表明LSTM-fine-tune 模型能够快速拟合,平均均方误差仅为 1.0335,明显低于直接预测误差1.5368.为通过实际监测数据检验本方法泛化能力,分别获取了试验条件下与实际服役环境下氧气浓缩器的数据,对模型的泛化能力进行验证.结果表明,迁移后训练集预测精度提高了 43.0%,测试集预测精度提高了20.2%.

In response to the current problems in the maintenance process of aircraft power systems, such as relying on traditional experience, poor troubleshooting robustness, and slow response of maintenance personnel to fault changes. In this paper, Knowledge graph is introduced into the field of aviation power supply health management, and troubleshooting experience is presented in the form of Knowledge graph to improve the correlation ability between fault data. This paper proposes a method to build a fault Knowledge graph for aircraft power system health management based on the data source of a certain aircraft power system manual and typical fault cases: first, build a power system data set according to the semi structural and unstructured data such as the aircraft power system manual and typical fault cases; Then, the Bert-BiLSTM-Ptr-Net deep learning algorithm was applied to extract power system fault entities from power system manuals and typical fault cases, and the superiority of this algorithm was verified by comparing it with the Bert-BiLSTM-CRF algorithm; Next, the BERT-BiLSTM deep learning algorithm is used to extract relationships between power system fault entities. Compared to other deep learning algorithms, this algorithm has a relationship extraction effect in aircraft power system manuals and typical fault case texts; Finally, Neo4j Graph database is used to visualize the fault Knowledge graph of aircraft power system, which effectively improves the troubleshooting ability of maintenance personnel and is convenient to guide the fault maintenance of aircraft power system.

For lack of research on degradation test（DT） for molecular sieve oxygen concentrator（MSOC） and clear account of the factors causing degradation failure of molecular sieve bed（MSB）, a DT design method of MSOC based on orthogonal test was proposed. Based on the working principle of MSOC, the main factors causing degradation failure of MSB were determined, and a DT system of the MSOC was built. The optimal test height for the DT was 9 km, as was determined and verified by several controlled tests. A DT scheme of MSOC was designed based on the orthogonal test idea. The influence of various factors could be obtained through 9 typical tests, which reduced the test times and costs, and improved the efficiency of coupling tests. The test design can simulate the real working environment, and can effectively reduce the impact of test factors on the initial value of the test results.