bluuebunny/crossref_metadata_2025_split huggingface dataset:
A fault detection of aero-engine rolling bearings based on CNN-BiLSTM network integrated cross-attention
Authors: Jiang Zhilei , Li Yang , Gao Jinke , Wu Chengpu 
Published: September 2024

Abstract
Aero-engine rolling bearings are essential for engine health, in which disruptive failures can be prevented and reduce great losses in air flight. To improve the efficiency of fault detection, an improved network, named CNN- BiLSTM -Cross-Attention (CBLCA) was proposed. The Bidirectional Long Short-Term Memory (BiLSTM) layer captures the temporal features as the input data. The cross-attention mechanism is integrated with the Convolutional neural networks (CNN) layer and the BiLSTM layer respectively. More important feature information can be identified with the CBLCA model. The proposed model was also validated with the open-sourced aero-engine rolling bearings data set. To improve the identification accuracy, a novel method that combines fast Fourier transform and Variational mode decomposition is used for the data preprocessing. Each original signal sample is transformed into a feature set containing richer information, and the number of features significantly increased in the entire dataset. Compared with some existing LSTM models, such as LSTM, BiLSTM, CNN-BiLSTM, and CNN-LSTM, the classification accuracy was increased by 55%, 54%, 5%, and 7%, respectively. The processing method for vibration signals and the CBLCA model can improve the accuracy and reliability of fault diagnosis for aero-engine rolling bearings.
A novel weighted-guided tensor completion missing data imputation method for health monitoring data of planar parallel mechanism
Authors: Wu Qiqiang , Zhang Xianmin , Zhao Bo 
Published: September 2024

Abstract
High-end mechanical equipment plays a crucial role in the manufacturing industry, making the monitoring of its operational status highly significant. Due to various factors such as environmental influences, the absence of monitoring signals in mechanical equipment status is a common issue, leading to a decline in data quality. To ensure data quality, this paper proposes an adaptive weighted low-rank tensor missing data imputation method. Firstly, based on the motion characteristics of a planar parallel mechanism (PPM), a new low-rank tensor model is established using periodicity, sliding windows, and time series. Secondly, the tensor truncation nuclear norm is defined, and a novel rate parameter is introduced to control the truncation degree of all tensor modes, thereby obtaining weights for each dimension. Finally, within the framework of the alternating direction method of multipliers, adaptive weights for each dimension are obtained during each iteration, completing the filling of missing data. Two types of missing patterns are studied on a PPM experimental platform, and the results showed that as the missing rate increased, the mean absolute percentage error is less than 0.018, and root mean square error is less than 0.001, and the rate of change is less than 0.08, which was significantly better than other compared algorithms.
Remaining useful life prediction based on time-series features and conformalized quantile regression
Authors: Mao Song , Li Xiaofeng , Zhao Boyang 
Published: September 2024

Abstract
The remaining useful life (RUL) prediction is a key task in the field of prognostics and health management (PHM) and plays a crucial role in preventive maintenance tasks. Traditional prediction methods have mostly focused on point prediction issues, neglecting the uncertain factors in the prediction task, thus failing to ensure the credibility of the prediction. In light of this, this paper focuses on improving the accuracy of point prediction models for RUL and interval prediction issues, proposing the introduction of multi-scale convolutional neural networks (MCNN), decomposed time-sequential linear layers (DL), and conformal quantile regression (CQR) techniques into the RUL prediction task of aero engines. The aim is to provide timely and accurate failure warnings for aero-engines, effectively ensure their reliability and safety, and reduce maintenance costs throughout their life cycle. In response to the limitations of current point prediction models in capturing the temporal features of life data, a MCNN-DL-based RUL prediction model is proposed to capture life data’s long-term trends and local variations for precise point predictions. Furthermore, an interval estimation approach for RUL is presented, which integrates the MCNN-DL model with CQR to account for prediction uncertainty. Finally, the method in this paper is verified using the commercial modular aero-propulsion system simulation (CMAPSS) dataset, and the results show that the method has achieved excellent results in both RUL point prediction and interval prediction tasks.
Prediction method for 4D trajectory of large fixed wing UAV under multi-layer convolution and bidirectional gating model
Authors: Ma Xin , Li Zixuan , Zheng Linxin , Lu Xikang 
Published: September 2024

Abstract
With the increase of low-altitude flight tasks, the importance of research on low-altitude flight safety and efficient operation has gradually become prominent. Therefore, it is necessary to try to establish a low-altitude airspace flight path pre-planning method based on 4D trajectory prediction, which can effectively support the above research. In order to improve the integrity, accuracy and reliability of the long-range trajectory prediction results of large fixed wing UAV (LFW UAV), based on the standard neural network as the basic concept to form a fusion model of improved convolutional neural network and bidirectional gated recurrent neural network. At the same time, the flight trajectory fitting preprocessing is added to build the completed multilayer cyclic convolutional improvement model (MCCI). The experimental results show that the MCCI model has obvious advantages in terms of prediction accuracy, deviation range and predictable duration when dealing with the LFW UAV 4D trajectory prediction problem, and better reflects the characteristics of 3D position and spatiotemporal elements. The comparative analysis shows that the error values of the MCCI model are smaller than those of the comparison prediction models in any dimension, and the prediction results have better fit and model performance.
A novel dynamic predictive maintenance framework for gearboxes utilizing nonlinear Wiener process
Authors: Dong Enzhi , Zhang Yu , Zhan Xianbiao , Bai Yongsheng , Cheng Zhonghua 
Published: September 2024

Abstract
In the context of advancing industrial automation, gearboxes, as pivotal components in power transmission systems, have a direct bearing on the operational efficiency and safety of the entire machinery. This study introduces a novel dynamic predictive maintenance (PdM) framework for gearboxes using a nonlinear Wiener process. Comprehensive experiments validate the framework, demonstrating significant reductions in maintenance costs and improvements in reliability. First, a full-life degradation experiment was executed on the gearbox, leveraging the root mean square value of the vibration signal as an indicator of system degradation. Subsequently, the signals from four vibration sensors were synthesized and normalized through kernel principal component analysis, thereby enabling a more nuanced representation of the gearbox’s degradation profile. The degradation trajectory was then modeled using a nonlinear Wiener process framework. The Wiener process’s parameters and state variables were iteratively refined utilizing an online filtering algorithm grounded in Bayesian inference. This facilitated the derivation of the probability density function for the remaining useful life (RUL), thereby enabling a robust prediction of the gearbox’s RUL. Finally, to minimize maintenance costs per unit of time, an optimization model for dynamic maintenance decision-making was formulated. The optimal maintenance timing was ascertained by solving this model. The empirical findings of this investigation demonstrate the efficacy of the proposed approach in executing dynamic PdM for gearboxes. This research endeavors to furnish novel theoretical underpinnings and pragmatic directives for the field of PdM in the context of gearboxes.
Hive-shaped carbon nanotubes coated with MoSe2 as an electrochemical sensor for cholesterol
Authors: Hu Lei , Huang Yu-Chiao , Huang Yung-Jui , Lin Pao-Hung , Wang Huan-Chun , Lin Chung-Kuang , Lee Kuei-Yi 
Published: September 2024

Abstract
The study utilized transition metal chalcogenide, molybdenum diselenide (MoSe2), for application in the field of bioelectrochemical sensing. The MoSe2 was combined with carbon nanotubes (CNTs) by chemical vapor deposition to enhance the specific surface area and improve the detection sensitivity. To further increase the contact area between the electrolyte and the electrode, photolithography techniques were employed to fabricate hive-shaped CNTs, thereby enhancing the specific surface area. Next, cholesterol oxidase (ChOx) was coated onto the electrode material, creating a cholesterol biosensor. Cyclic voltammetry was utilized to detect the concentration of cholesterol. The experiment involved segmented testing for cholesterol concentrations ranging from 0 μM to 10 mM. Excellent sensitivity, low detection limits, and high accuracy were achieved. In the cholesterol concentration range of 0 μM–100 μM, the experiment achieved the highest sensitivity of 4.44 μAμM

⋅

cm−2. Consequently, all data indicated that ChOx/MoSe2/CNTs functioned as an excellent cholesterol sensor in the study.
Unconventional free fall measurements of g: involving the Pythagorean Theorem and inverse tangent function
Authors: Pili Unofre B 
Published: September 2024

Abstract
The current work presents unconventional but instructionally useful approaches to data analysis and modelling that allow for measurements of the acceleration due to gravity, g, using free-falling motion and the popular Tracker software. The two techniques detailed in this article illustrate the employability of the Pythagorean Theorem and the inverse tangent function in modelling free-falling motion that likewise, expectedly, permit for the determination of g. These two mathematical concepts are brought to relevance by strategically situating the origin of the coordinate system at an appropriate point. The models returned values of the gravitational acceleration that are comparable to that by the traditional technique, the one directly based on the vertical position-time data. Additionally, the unconventional free-fall data analyses presented in this paper suggest extra and insightful activities for the students, exercises on a notably less-automated nonlinear curve fitting on a spreadsheet (MS Excel) at most. Ultimately, by applying familiar mathematical concepts associated with a right triangle to nontraditional contexts like free-fall, this laboratory or lecture activity is perceived to spark greater interest of the students and enhance learning by unifying theory and experimental data.
Cronpes: time measurement device suitable for teaching experimental physics in Brazil
Authors: Bulla M A , Oliviera T , Franco M K K D , Yokaichiya F 
Published: September 2024

Abstract
Several public schools and also universities in Brazil have few or none financial resources to acquire certain equipment for teaching physics. Nevertheless, it is extremely important that institutions, schools and universities offer laboratory conditions for the students, mainly in engineering courses, in order to get the first contact with physics concepts in practice. For this purposes, it was proposed to develop a simple construction, low-cost and easy maintenance photogate (Cronpes) device, so that teachers, technicians and even students from the basic and high education network can build them for their schools, institutions and universities, thus improving the quality of teaching physics. This study aims to implement a photogate Esp32 microcontroller based programmed in MicroPython with good resolution, accuracy and user friendly in order to apply in experiments like simple pendulum, spring oscillations and conservation of mechanical energy. The main focus of this research was to demonstrate, through the development of Cronpes, that we can create solutions for educational laboratories, in a viable, creative and inexpensive way.
Effect of atomic layer deposition process parameters on TiN electrode for Hf0.5Zr0.5O2 ferroelectric capacitor
Authors: Li Hongbo , Zhang Jian , Guo Chongyong , Liu Yuanya , Liu Chunyan , Wang Yu , Li Jianjun , Yuan Hui , Jin Xingcheng 
Published: September 2024

Abstract
Hf0.5Zr0.5O2 (HZO), an innovative and exceptional ferroelectric material, exhibits remarkably high sensitivity, making it particularly vulnerable to electrode effect. Titanium nitride (TiN) is a commonly employed as electrode material in the complementary metal–oxide–semiconductor process. Optimizing the process parameters of preparing TiN film can alter matching degree with HZO capacitor, so as to find the optimal parameters of TiN process to improve ferroelectric property of HZO. In this study, the impact of key process parameters in atomic layer deposition (ALD) TiN, including cycle number, TiCl4 and NH3 pulse time, process temperature (T
p) on film thickness, crystalline phases of TiN, square resistivity (R
s), surface average roughness (R
a) and the root-mean-square roughness (R
q) of TiN film are comprehensively investigated. Through optimization, ∼10 nm ALD TiN film can achieve excellent uniformity of 0.43%, low R
s of 286.9 Ω/□, improved R
a and R
q of 1.82 Å and 2.28 Å. The results show that the maximum 2 times remnant polarization (2P
r) of the HZO ferroelectric capacitor with optimized TiN electrodes can reach 35.17 µC cm−2, and the switching cycle endurance exceeds 8 × 107.
The effect of temperature on the electrical characteristics of zigzag and armchair black phosphorus based 2D MOSFET
Authors: Majumder Tanmoy , Mukherjee Chandrima , Dasgupta Sudeb , Chakraborty Udayan , Das Narottam , Bhattacharjee Abhishek 
Published: September 2024

Abstract
A first time comparative study of the thermal dependence of vital electrical characteristics of two-dimensional metal-oxide-semiconductor field-effect transistors based on black phosphorus for both zigzag and armchair orientations is presented in this paper. It is seen that a higher in-plane thermal conductivity in zigzag direction results in a much better on state current performance which comes at the cost of orders of magnitude increase in gate leakage and a reduced on to off state current ratio. The effect of temperature on threshold voltage (V
TH), short channel effects like drain induced barrier lowering, subthreshold swing (SS), Schottky barrier height ΦSB and transconductance behavior in both zigzag and armchair orientations is thoroughly discussed and the inherent physical mechanisms resulting the variations are also presented. Though increase in temperature is found to deteriorate the SS and drain conductance but at the same time, it is found to improve the short channel performance of the devices under consideration.
An IGBT coupling structure with a smart service life reliability predictor using active learning
Authors: Feng Shizhe , Guo Yicheng , Li Weihua , Du Haiping , Krolczyk Grzegorz , Li Z 
Published: September 2024

Abstract
An effective approach is proposed to evaluate the service life reliability of a multi-physics coupling structure of an insulated gate bipolar transistor (IGBT) module. The node-based smoothed finite element method with stabilization terms is firstly employed to construct an electrical-thermal-mechanical (ETM) coupling structure of the IGBT module, based on which the multi-physics responses can be accurately calculated to predict the service life of the IGBT module. By using the high-quality sample data obtained through the ETM coupling model, a Monte Carlo based active learning Kriging metamodel (AK-MCS) is developed to assess the service life reliability of the IGBT module, which can greatly reduce the computational cost needed by the surrogate model construction and reliability analysis. Numerical results show that the proposed ETM coupling structure can produce high-quality sample data of the IGBT dynamics and the AK-MCS machine learning technique can accurately estimate the service life reliability of the IGBT module.
Dynamic stability and response of morphing wing structure with time-varying stiffness
Authors: Prabhakar Manoj , Murugan Senthil 
Published: September 2024

Abstract
Morphing, adaptable or smart structures are being used in mechanical and aerospace applications in recent years. These structures often have the property of time-varying stiffness or inertial properties, which can cause parametric instability issues that are not well understood. This paper examines the dynamic stability and response of a morphing aircraft wing with periodically time-varying structural stiffness. The wing is modeled as a beam with coupled bending-torsion motion, and parametrically excited stiffness. Aerodynamic loads introduce aerodynamic damping and aerodynamic stiffness to the wing structure. The dynamic and aeroelastic equation of motion resembles a coupled, damped Mathieu-type equation but differs with asymmetric damping and stiffness matrices, and symmetric inertial matrix. Further, these equations are functions of airspeed, magnitude and frequency of parametric excitation. Initially, dynamic stability of the wing is analyzed using Floquet theory, and the instability regions are numerically quantified by stability charts. Subsequently, dynamic responses in the stable and unstable regions are investigated with a Floquet-based Harmonic balance method. The findings reveal that, at zero airspeed, the combination instabilities are eliminated by varying the bending and torsional stiffness with equal magnitudes and frequency. However, as airspeed increases, instability regions shift unevenly, leading to the emergence of new instabilities. Furthermore, the response analysis within stable regions uncovers several unfavorable zones for operating the variable stiffness, where response decay is slower. The results clearly show that parametric excitation can cause unusual phenomena that significantly impact the operation of morphing wings with variable stiffness, which needs to be thoroughly investigated for successful implementation.
Multifunctional 1D/2D silver nanowires/MXene-based fabric strain sensors for emergency rescue
Authors: Xie Jiayu , Zhang Ying , Wang Huajun , Liu Qingqing , He Jingqiang , Guo Ronghui 
Published: September 2024

Abstract
Monitoring the vital signs of the injured in accidents is crucial in emergency rescue process. Fabric-based sensing devices show a vast range of potential applications in wearable healthcare monitoring, human motion and thermal management due to their wearable flexibility and high sensitivity. Nevertheless, flexible electronic devices for both precise monitoring of health under low strain and motion under large strain are still a challenge in extremely harsh environment. Therefore, development of sensors with both high sensitivity and wide strain range remains a formidable challenge. Herein, a wearable flexible strain sensor with a one-dimensional/two-dimensional (1D/2D) composite conductive network was developed for healthcare and motion monitoring and thermal management by coating 1D silver nanowires (AgNWs) and 2D Ti3C2T
x
MXene composite films on nylon/spandex blended knitted fabric (MANS). The MANS strain sensor can simultaneously achieve high sensitivity (gauge factor for up to 267), a wide range of detection (1%–115%), excellent repeatability and cycling stability (1000 cycles). The sensor can be utilized for human health monitoring including heartbeat, pulse detection, breathing and various human motion. Moreover, the MANS sensor also has the electrical heating properties and voltage control temperature between 20 °C–110 °C can achieved at low voltage. In addition, the MANS shows hydrophobicity with water contact angle of 137.1°. The MXene/AgNWs composite conductive layer with high sensitivity under low and large strains, electrical thermal conversion, and hydrophobicity has great potential for precisely monitoring health and motion of the injured in emergency rescue in harsh environment.
Research on a frequency-increasing piezoelectric wave energy harvester based on gear mechanism and magnetic rotor
Authors: Liu Renwen , Yang Bowen , Fan Wei , Liu Zheming , Wang Chensheng , He Lipeng 
Published: September 2024

Abstract
Wave energy is a widespread clean energy source, but harvesting low-frequency wave energy efficiently remains a challenge. In this paper, a frequency-increasing piezoelectric wave energy harvester (FPWEH) based on gear mechanism and magnetic rotor is proposed. The gear mechanism transforms the vertical motion of the wave into the higher-frequency rotational motion of the magnetic rotor. The magnetic rotor is equipped with several rotating magnets and one revolution of the magnetic rotor enables multiple excitations of the piezoelectric cantilevers. Therefore, the wave excitation frequency is increased, so that the FPWEH can obtain better output performance. The major factors influencing output performance are determined through theoretical and simulation analysis, and a test system to simulate the wave environment is established. According to experimental findings, the FPWEH can generate an output voltage of 69.82 V and a maximum power of 28.33 mW when the external resistance is 20 kΩ. It can also successfully power thermohygrometer and light-emitting diodes. These results validate the feasibility of the FPWEH for providing electricity to electronics with low power requirements. This research also offers a novel approach to harvesting low-frequency wave energy.
A high throughput facility for the RF characterisation of planar superconducting thin films
Authors: Seal D , Malyshev O B , Goudket P , Sian T , Gurran L , Valizadeh R , Marks H , Pattalwar S , Pattalwar N , Pira C , Chyhyrynets E , Burt G 
Published: September 2024

Abstract
Accelerator laboratories worldwide are researching copper radio frequency (RF) cavities coated with superconducting thin films to exceed the limits of bulk niobium. The development and RF testing of thin films on small planar samples is vital before cavity depositions. A team at Daresbury Laboratory have developed a cost-effective facility using a novel 7.8 GHz Choke Cavity for the RF characterisation of planar samples. RF chokes ensure that no electrical contact is required between the sample and the cavity. The main advantages are: a simple sample design (90–130 mm diameter disk with no sample-cavity welding) and easy operation using a LHe-free cryostat. This enables high sample throughput, with up to 3 sample tests per week, making the facility suitable for quick, systematic scanning of deposition parameters. With the sample thermally and physically isolated from the test cavity, it is possible to measure the average surface resistance,

R

s

, directly using an RF-DC compensation method. Facility commissioning has been performed with bulk and thin film niobium samples. These tests have demonstrated the ability to measure

R

s

at temperatures in the range 4–20 K and sample peak magnetic fields up to 3 mT. At present, the minimum resolvable

R

s

is 0.5

μ
Ω

with typical uncertainties of 9%–15%. The design, operation and commissioning of this facility is reported in this paper.