bluuebunny/crossref_metadata_2025_split huggingface dataset:
Solvability of fractional differential equations with applications of Morgan Voyce polynomials
Authors: Kumar Sandeep , Singh Vikram , Kumar Umesh 
Published: June 2024

Abstract
In this research, a new computational approach is presented to address multi-order fractional differential equations, including the renowned Bagley-Torvik and Painlevé equations. These equations are pivotal in scientific and engineering realms, like modelling the movement of a submerged plate restricted in a Newtonian fluid and gas in a fluid, along with simulating the coupled oscillations. We utilise the collocation approach employing a novel operational matrix derived for Morgan-Voyce polynomials via the Atangana-Baleanu fractional derivative. Initially, we introduce the fractional differential matrix to convert the problem and its constraints into a system of algebraic equations with unknown coefficients. These coefficients aid in finding numerical solutions for the given equations. To assess the efficiency of proposed method, various examples are simulated utilising the proposed approach and the outcomes are compared with existing results for different derivatives.
Temperature-controlled tunable metamaterial absorber based on vanadium dioxide with ‘switch’ functionality
Authors: Zhu Jun , Pan Zhe , Tang Jian 
Published: June 2024

Abstract
The performance of traditional absorbers is fixed in a specific frequency or wavelength range, and the actual application often needs to adjust the absorption characteristics according to different scenarios or needs. A THz wave modulator, utilizing temperature-controlled phase change materials, is proposed to address the limitation of absorbers’ inability to adjust to external environments. Tunable absorber is a kind of device with dynamic regulation ability, and its absorption characteristics can be adjusted and optimized according to external conditions. This modulator enables the switch function of metamaterial absorbers, comprising a gold reflector layer, a silicon dioxide depletion layer, and a vanadium dioxide pattern layer. Simulations via finite element method reveal two nearly perfect absorption peaks, up to 99.99%. As temperature rises, absorption rates increase, stabilizing gradually after vanadium dioxide transitions from insulating to metallic phase. With a modulation depth of 98.49%, the absorber achieves adjustability. It enables polarization-independent absorption of electromagnetic waves, exhibiting strong absorption at incident angles from 0° to 50° for TE and TM waves. Leveraging vanadium dioxide’s phase change characteristics, the absorber can switch between ON and OFF states based on temperature changes, promising potential applications in light modulation and THz absorbers.
Sequentially Quadratic Surrogate Algorithm for Time-dependent Reliability and Reliability Sensitivity Analysis
Authors: Suting Zhou , Jie Liu , Lin Zhang , Zhenzhou Lu , Changhua Nie 
Published: June 2024

Abstract
Time-dependent reliability and reliability sensitivity analysis in presence of random uncertainty is widespread in equipment structures. To this end, this paper establishes a sequentially quadratic surrogate method. Firstly, the global reliability sensitivity analysis (GRS) is transformed into the classification problem of the time-dependent performance function outputs by means of conditional probability formula. Secondly, referring to the strategy of the Meta-IS method, the Kriging model of time-dependent performance function is employed to construct the importance sampling function to generate the importance sampling (IS) samples of failure domain efficiently. Furthermore, the Kriging model is updated in the IS samples set through the single-loop adaptive Kriging method to realize the accurate identification of the failure indicator function of IS samples, as well as simulation of time-dependent failure probability. Finally, utilize the information of the failure samples obtained by the estimation of time-dependent reliability to evaluate GRS. The proposed algorithm has excellent computational efficiency and applicability due to the conversion of the conditional probability formula, which enables the computational consumption of the time-dependent reliability and GRS analysis independent of the dimensions of the inputs, as well as the Meta-IS method, which improves the sampling efficiency and is applicable to the case of complex implicit performance function. The given examples fully verify the conclusions.
Study on fault characteristic of piezoelectric acceleration sensor
Authors: Guo Ziwen , Zhang Chengcheng , Zhang Xiangyu , Li Yongbo , Deng Zichen 
Published: June 2024

Abstract
In order to explore the fault characteristics and failure mechanism of the sensor, staring from the working principle and structural characteristics of the piezoelectric acceleration sensor, the problems affecting the quality of the collected signal due to the fault of the component of the sensor are systematically studied. Firstly, according to the application scenario and structural characteristics of this kind of sensor, the main fault forms are analyzed, including: insufficient preload, crystal plate damage, crystal plate side loading and crystal plate phase loading reverse four kinds of faults. Secondly, the signal of the exciter is used to test the above sensors. It is found that except the sensor with insufficient preload does not affect the signal, other fault sensors will make the collected signal amplitude smaller, but the signal frequency does not change. In order to verify the accuracy of the exciter test results, the sensor signal test on rotor bearing vibration was continued. The test results show that not only the signal amplitude decreases, but also the high frequency part of the signal is seriously distorted. Finally, the fault characteristics of various components of piezoelectric acceleration sensor are obtained by combining the test results of the two groups.
A prediction method for aircraft icing severity forecast based on probability model and meteorological observation data
Authors: Liu Huan , Wang Yi , Lei Li , Liu Zheyuan , He Xindang 
Published: June 2024

Abstract
Aircraft flight performance and safety are inevitably affected by adverse meteorological conditions, one such weather is icing. Aircraft icing can cause severe aerodynamic and flight mechanical effects, thus threatens aircraft flight safety. This study presents an aircraft icing severity prediction method with probabilistic model, in which meteorological observation data are described as random variables and the airplane icing severity level is defined as fuzzy variables with membership function. When the information of aircraft flying cloud type, altitude, speed, temperature, pressure and dew point temperature is known, the probability of aircraft icing severity during aircraft fly can be effectively predicted by a weighted icing forecasting method. Using the proposed method into two aircraft icing cases, the analysis results show that the proposed method is accurate and valid. It can provide technical support for aircraft icing forecasting.
Advancing UAV Sensor Fault Diagnosis based on Prior Knowledge and Graph Convolutional Network
Authors: Su Zhe , Zhang Yulong , Zhu Jun , Li Yongbo 
Published: June 2024

Abstract
Unmanned Aerial Vehicles (UAVs) are commonly equipped with multiple sensors to facilitate control and navigation. However, due to the complexity of the flight environment, the UAV sensors are prone to damage, which can cause serious accidents or even economic losses. Therefore, it is of great importance to propose an effective UAV sensor fault diagnosis method. The following weaknesses are obtained by the existing fault diagnosis methods: 1) The model-based methods, such as parameter estimation, state estimation, and equivalent space method, require introducing high-precision system models and expert knowledge. 2) Conventional graph convolutional network (GCN)-based methods fail to guarantee the accuracy of association graphs. To address the above two challenges, the dynamics model of quadrotor as prior knowledge is proposed in this paper to construct the association graph first. Then, a spatial-temporal difference graph convolutional network (STDGCN) consisting of a difference layer and multiple spatial-temporal graph convolutional modules is built. The difference layer enhances the feature extraction of the graph nodes, while the spatial-temporal graph convolutional modules extract the spatial-temporal dependencies of sensor data. Finally, the robustness and effectiveness of STDGCN are demonstrated in fault diagnosis compared with the commonly used data-driven methods. The experimental result shows that the proposed STDGCN method exhibits outstanding feature extraction ability with the highest diagnostic accuracy.
Method of aero-engine flight safety pre-warning based on support vector machine
Authors: Dong Jiang , Zhang Qiangbo , Guo Haidong 
Published: June 2024

Abstract
Engine vibration is a decisive factor in flight safety. Aiming at the problem that the current single vibration warning value of an aero-engine cannot reflect the vibration characteristics of the engine in flight, the estimated probability density of the measured vibration of the engine based on the support vector machine (SVM) method is established, and the aero-engine flight safety pre-warning research is carried out. Through the statistical analysis of the installed flight data of a engine, the probability density function estimation of the measured vibration data of the aero-engine flight was carried out by the support vector machine, and the vibration warning value of the installed engine in each state was determined according to the probability distribution and the general primary side determined by the vibration monitoring limit value of rotating machinery. Through the verification of actual flight data, the determined vibration warning value with the change trend of engine state is consistent with the actual flight data, and is more sensitive to engine failure, which is conducive to improving the early failure warning ability of aero-engine during flight.
Improvement of drilled holes quality by voice-activated ultrasonic vibration assisted drilling process of different orientation
Authors: Patwari Anayet U , Navid Wasib Ul , Morshed Rahman Maruf , Showkhin Sakib , Bhuiyan Shah Alam 
Published: June 2024

Abstract
Ultrasonic drilling enables drilling in difficult-to-machine materials, including ceramics and composites, with greater accuracy and precision. The localized and controlled energy application of ultrasonic frequency minimizes the risk of material fracture, cracks, or delamination. This capability ensures that the drilled hole maintains its intended dimensions, shape, and integrity, resulting in improved hole accuracy and overall drilled hole condition. Ultrasonic drilling is an advanced machining technique that utilizes high-frequency vibrations to improve the drilling process. In this designed voice-activated module, electrical energy has been converted into mechanical vibrations through piezoelectric transducers, generating ultrasonic waves in the range of 20 kHz to 80 kHz. Experiments has been conducted considering different process parameters with the changing different orientations of the emitters-horizontal and vertical positions and comparison was done with no vibration conditions. It has been observed that with the application ultrasonic vibration frequency the surface roughness improved by 45% in horizontal position, 28% in vertical position compared with no vibration conditions-conventional techniques. It has also been observed that the circularity index of the drilled holes also improved by 31% for horizontal and 7.6% for vertical position.
Dynamic Simulation Analysis and Research of Overhang Cold Launching System
Authors: Wang Yiwei , Li Jiasheng , Zhou Yuanchun , Yin Xiang , Liu Guang , Xu Bin 
Published: June 2024

Abstract
In order to study the structural dynamic response and the separation rule of launch tube and missile of the overhang cold launching system during launching, and provide a basis for the design of new overhang launching system, a dynamic model of the overhang cold launching system was established by using the rigid-flexible coupling method and multi-body dynamics according to the structural characteristics and ejection physical process of the overhang cold launching system. The accuracy and rationality of the modeling method proposed by this paper were validated against the test. Based on this, the dynamic response of ejection and outlet attitude were analysed. The results show that the dynamic model of overhang cold launching system can accurately reflect the vibration characteristics of the system. A large longitudinal displacement of the center of launch tube will occur during the ejection process. The oscillation frequency is relatively small, but the structural vibration caused by the ejection impact will have a great influence on the pitching attitude of the missile. The vibration response of the adjacent launch tube is small, which is beneficial for the continuous launching of the overhang launching system. The research results can provide a basis for the subsequent design of vibration reduction.
Instability Feature Extraction Method of Compressor Based on Sample Autocorrelation Function
Authors: Tian Lin , Tao Ye , Zhang Shaoli 
Published: June 2024

Abstract
The stability of aero-engine compressor is related to flight safety. Severe instability may lead to engine surge, parking or mechanical damage and even cause flight accidents. Aiming at this problem, a compressor instability feature extraction method based on sample autocorrelation function is proposed, and an autocorrelation analysis model of fluctuating pressure is established. The model is verified and analyzed by using the flight test data of an engine, which shows the feasibility of this method. It can provide technical support for real-time monitoring and quantitative evaluation of aero-engine compressor stability and improve safe flight efficiency.
Sparse Fast Fourier Transform Analysis of Aero-Engine Vibration Data
Authors: Jia Hao , Zhang Qiang , Wei Mengyuan , Sun Xiewen 
Published: June 2024

Abstract
Sparse fast Fourier transform is a new spectrum analysis method based on signal sparse characteristics in recent years. Sparse fast Fourier transform can quickly and accurately process large data by identifying and discarding frequency signals that have no effect on the analysis results. In order to explore the practicality of Sparse fast Fourier transform in aero-engine vibration data analysis, this paper combines the actual flight test vibration data, and studies the sparse fast Fourier transform method and the traditional fast Fourier transform method in terms of spectral characteristics, running time, algorithm stability. By comparison, it can be found that the sparse fast Fourier transform algorithm can greatly improve the efficiency of vibration data analysis while ensuring accuracy.
End-to-end Temporal Transformer with Autocorrelated Attention Mechanism Augmented for Remaining Useful Life Prediction of Turbopump Bearings
Authors: Chen Zhuohang , Chen Jinglong , Pan Tongyang , Luo Qinyuan 
Published: June 2024

Abstract
Accurately predicting the remaining useful life (RUL) has become crucial for ensuring stable and safe operations for rocket engines due to the extreme working environment. However, current RUL prediction approaches based on convolution and recurrent frameworks lack effective feature extraction methods to model long-term dependencies, resulting in limited accuracy and generalizability. To address this issue, we propose an end-to-end temporal Transformer with autocorrelated attention mechanism augmented for RUL prediction of turbopump bearings. The Transformer module is adopted as the backbone of proposed framework to model long-term dependencies from the raw signals. To further enhance predictive capability, we develop a self-attention mechanism based on autocorrelation calculation. This mechanism extracts and aggregates feature representations through similarity comparison at the sub-series level. Furthermore, we utilize convolutional layers with residual links to capture internal detail features, compensating for the limitations of capturing local information. The proposed framework is evaluated through a life-cycle rocket engine bearing dataset and the experimental results demonstrate the effectiveness and superiority on RUL prediction.
Research on transfer learning diagnosis method for planetary gearbox bearings based on variational embedding composite multiscale diversity entropy and manifold dynamic distribution adaptation
Authors: Guo Hang , Wang Xianzhi , Chen Gaige , Ma Hongbo 
Published: June 2024

Abstract
To improve the generalisation ability of traditional machine learning fault diagnosis model, this paper proposed a planetary gearbox bearing diagnosis method based on the variational embedded composite multiscale diversity entropy (veCMED) and manifold dynamic distribution adaptation (MDDA). Firstly, fault features are extracted using variational embedded composite multiscale diversity entropy (veCMDE). Secondly, the extracted entropy fault features are combined with manifold dynamic distribution adaptation (MDDA) of the flow shape to map the entropy values of different distributions to the space of the same distribution. Thirdly, the entropy value after feature mapping is input into the KNN classifier for fault identification. The final experimental results show that the proposed veCMDE and MDDA based transfer learning method has the best generalisation ability compared to seven transfer learning methods.
Autonomous Mission Planning for Spacecraft On-orbit Service Based on Hybrid Variant Particle Swarm Optimization
Authors: Wang Cheng , Lyu Yueyong , Zhao Xuebo , Hao Siqiang 
Published: June 2024

Abstract
For the complex on-orbit service (OOS) mission planning problem, an improved hybrid variant particle swarm optimization (PSO) method was proposed to improve the fuel utilization efficiency of spacecraft. Firstly, considering the time and fuel constraints, a model for OOS mission planning under the scenario of multiple target spacecrafts is established. The Lambert orbit maneuver method is used and the problem is transformed into a large-scale hybrid nonlinear integer programming problem. Next, an improved hybrid variant PSO algorithm is designed to optimize the fuel performance cost, and its simple structure and lightweight operation facilitate autonomous planning, considering the limited computing power of the onboard computer. The hybrid algorithm is composed of discrete particle swarm optimization (DPSO) and PSO, so it has a strong ability to search for the optimal service sequence and orbit maneuver time simultaneously. Moreover, the adaptive and variant operators are used to improve the searching ability and avoid falling into local optimal solutions. Finally, simulation experiments show that the proposed method can quickly give optimal results, reducing fuel consumption effectively in the OOS mission and improving the OOS capability of the spacecraft.
Close-range maneuver planning for uncontrolled rendezvous with multiple elliptical orbit targets based on genetic algorithm
Authors: Xiao Fuhong , Sun Yanhong , Wang Rixin 
Published: June 2024

Abstract
On-orbit debris removal is an important and challenging problem in space engineering. A low-cost method for this problem is to use uncontrolled rendezvous with a sub-spacecraft released by a service spacecraft. The sub-spacecraft do not carry any control system and are designed to rendezvous with the target debris to remove it. This paper presents a comprehensive study on the mission planning. First, we analyze and dynamically model the uncontrolled rendezvous process for elliptical orbit targets, and study various aspects such as maneuver, sub-spacecraft release parameter solution, and rendezvous accuracy estimation. Then, we use NSGA-II, a multi-objective optimization genetic algorithm, to establish the mission planning model, focusing on the design of decision variables, constraints and fitness functions. Finally, we apply our proposed method to two specific cases of Molniya orbit, and verify the effectiveness of the planning model. We also conduct a deep analysis of the planning results, and summarize the commonalities and differences of various effective strategies.
Defect Detection in Civil Structure Using Deep Learning Method
Authors: Zhou Xiaoling , Tiong Robert L. K. 
Published: June 2024

Abstract
With the usage time, undesirable defects like crack and spalling appeared in various types of civil structures including beam; wall and column. These defects have a great influence on the usage life of the structures and hence need attention and maintenance. Normally, defect detection in civil structures is the human visual inspection. The manual approach is time-consuming and has a low accuracy. In this paper, we use deep learning method to predict multiple defects in various types of civil structures. Crack and spalling are recognized using the convolutional neural network model in a pixel level. To build the inspection model, the U-net Plus Plus architecture is used for segmentation. In the dataset, 1300 images are taken for training and 600 images for validation. The obtained computer vison model is tested on a dataset of about 491 images. The recall for crack and spalling reach 0.89 and 0.85 respectively. This research work develops an automated approach for defects detection in civil structures. The results can provide helpful guidance for structure health monitoring and maintenance in civil engineering like buildings and bridges.
Deep LSTM-based autoencoder for CFRP damage imaging using Lamb wave and correlation factor weighted delay-and-sum algorithm
Authors: Ji Dingcheng , Lin Jing , Gao Fei 
Published: June 2024

Abstract
The ultrasonic Lamb wave testing (ULWT) has proven valuable in non-destructive testing (NDT) due to its high sensitivity and wide coverage. However, the classical post-processing algorithm, the delay-and-sum (DAS) technique, suffers from notable artifacts and inadequate accuracy in Lamb wave inspection due to the existence of reflection and superposition, especially on composite materials. In this study, we proposed a novel algorithm, the correlation factor weighted DAS imaging algorithm based on the Long short-term memory-autoencoder (LSTM-AE), to address these deterioration issues. The LSTM-AE demonstrates the capability to extract potential features and accurately reconstruct input signals. By incorporating the concept of anomaly detection, an LSTM-AE trained with intact signals produces a significantly distorted output when exposed to damaged signals as input. We formulated a novel damage index (DI) based on the Pearson Correlation Coefficient (PCC) between the output and input signals as the weighting factor. The proposed method has undergone experimental validation, confirming its effectiveness in Lamb wave inspection.
Simulation of Internal Flow Field in Pulse Thruster Unit
Authors: Sun Junli , Long Dafeng 
Published: June 2024

Abstract
Pulse thrusters are widely used in the attitude control and flight trajectory control of rotating ammunition and vertical launching ammunition, which use own pulse impulse to change the attitude and trajectory of the missile. This paper simulates the internal flow field of a double-layer pulse thruster unit (PTU). The internal flow field of the PTU is simulated at various temperatures based on the three-dimensional compressible N-S governing equations and k-ω SST turbulence model. In addition, the influence of nozzle throat length and divergence half-angle on the performance of pulse T-thruster is analyzed. The simulation results demonstrate that the thrust output error of the PTU is minimum, when the nozzle throat length of the internal pulse thruster is 38 mm. Moreover, the stability of the PTU is improved.