bluuebunny/crossref_metadata_2025_split huggingface dataset:
A hybrid fault diagnosis method for rolling bearings based on GGRU-1DCNN with AdaBN algorithm under multiple load conditions
Authors: Sun Lirong , Zhu Xiaomin , Xiao Jiannan , Cai Wei , Ma Qianxia , Zhang Runtong 
Published: March 2024

Abstract
The fault diagnosis of rolling bearings is a critical aspect of rotating machinery, as it significantly contributes to the overall operational safety of the mechanical equipment. In the practical engineering environment, the complex and variable working conditions, along with the presence of overlapping noise, contribute to intricate frequency information in the acquired signals and their highly time-dependent characteristics, which makes it difficult to extract the available fault features hidden in the signal. Based on this, a hybrid fault diagnosis method named GGRU-1DCNN-AdaBN is introduced, which combines improved gap-gated recurrent unit network (GGRU), one-dimensional convolutional neural network (1DCNN), and adaptive batch normalization (AdaBN). The proposed approach involves several parts to enhance fault diagnosis accuracy in vibration signals under constant load conditions and variable load conditions. Firstly, the end-layer structure of the traditional GRU is replaced with a one-dimensional global average pooling layer to aggregate the influence components of defects and reduce model training parameters. Secondly, the fusion of different types of frequency and sequence features is achieved by combining 1DCNN, addressing the limitation of a single network’s feature extraction capability and the loss of temporal features in a cascaded hybrid model. Subsequently, the fused features are input into a softmax multi-classifier to obtain fault type identification results. Lastly, the GGRU-1DCNN method is further improved by incorporating the AdaBN algorithm, enhancing the model’s domain adaptive capability under variable load conditions and noisy environments. The method is validated using datasets obtained from Case Western Reserve University, aero-engine bearings, Xi’an Jiaotong University, and the Changxing Sumyoung Technology. The findings suggest that the proposed method demonstrates superior accuracy and robustness in fault diagnosis, as well as excellent generalization capability and universal applicability.
Study of partial discharges measurement cycles effect on defect recognition for underground cable joints
Authors: Chang Chien-Kuo , Boyanapalli Bharath Kumar 
Published: March 2024

Abstract
This study investigates the influence of various partial discharge (PD) measurement durations on defect recognition in high voltage power cable joints, aiming to address a crucial challenge in the field of insulation assessment. The main objective is to determine the optimal measurement cycle duration for accurate defect recognition, thereby enhancing the reliability of PD-based diagnostic techniques. Totally 14 cable joints, each containing three different types of prefabricated artificial defects, were analyzed across five different measurement cycle durations: 40, 80, 120, 200, and 1200 cycles. Subsequently, convolutional neural networks (CNNs) were employed for defect recognition analysis. The results reveal a significant impact of measurement cycle duration on defect recognition accuracy. Particularly, a CNN based on 200 measurement cycles demonstrates superior performance compared to models with fewer cycles, achieving a total defect recognition accuracy of 100%. This finding underscores the importance of sufficient measurement cycles for obtaining comprehensive PRPD patterns and accurate defect classification. Furthermore, the study highlights the significance of setting a threshold value to mitigate false conditions in defect type recognition, offering valuable insights for practical applications in power system maintenance and diagnostics. Overall, this research contributes to advancing the understanding of PD-based insulation assessment techniques and provides practical recommendations for optimizing measurement cycle duration to enhance defect recognition accuracy in high voltage power cable joints.
Vibration characteristics of defective axle box bearings in high-speed trains under track irregularity excitation
Authors: Wang Min , Yang Shaopu , Liu Yongqiang , Guo Tao , Chen Yanhong , Wang Baosen 
Published: March 2024

Abstract
Track irregularity is a type of excitation source for the wheel-rail system, and it is also the major cause of vibration and wheel-rail forces on the axle box bearings of vehicles, and it is commonly found on in-service lines. Therefore, this paper mainly focuses on the dynamic characteristics of high-speed train axle box bearings under track irregularity. Firstly, a nonlinear system containing a faulty double-row tapered roller bearings is established, the obtained equations of motion are solved numerically using the Runge–Kutta and comparing it with the varying compliance vibration frequency and fault characteristic frequency obtained by formula, the comparison results verify the effectiveness of the model. Then, the track irregularity is coupled to the above bearing system, the model is also verified by using the rolling and vibrating test rig of single wheelset. Secondly, the eigenvalue of covariance matrix is introduced as an index to analyze the influence trend of different fault sizes, loads, and inner rotational speeds on the axial trajectory. Finally, the influence trend of track irregularity on the stability of vertical acceleration is analyzed by simulating different operating conditions.
End-to-end information fusion method for transformer-based stereo matching
Authors: Xu Zhenghui , Wang Jingxue , Guo Jun 
Published: March 2024

Abstract
In stereo matching, the application of transformers can overcome the limitations of disparity range and capture long-range matching information. However, the lack of cross-epipolar context information often leads to numerous mismatches, especially in low-texture regions. An end-to-end information fusion stereo matching method is proposed to address this issue. In the proposed method, a feature extraction method that combines dense connections and a residual block is proposed. Global and local semantic information can be effectively fused by incorporating dense connections among multiscale feature maps. Additionally, the inclusion of a residual block helps extract more representative feature maps. The idea of criss-cross attention is introduced in the transformer implicit matching process. Criss-cross attention enables the capture of cross-epipolar context information by combining horizontal and vertical attention mechanisms. This method improves the matching accuracy from the perspective of multi-path information fusion. According to the matching results, the disparity regression layer and the context adjustment layer are used to generate the initial and final disparity maps, respectively. The proposed method is evaluated on the Scene Flow, KITTI 2012, and Middlebury 2014 datasets. Experimental results indicate that the proposed method effectively enhances matching accuracy. Moreover, the proposed method exhibits strong generalization ability, allowing for direct application to synthetic, real outdoor, and real indoor scene images.
Transient N-GQDs/PVA nanocomposite thin film for memristor application
Authors: Pisal Deshmukh Akshaya , Patil Kalyanee , Barve Kanchan , Bhave Tejashree 
Published: March 2024

Abstract
In recent years quantum dot (QDs) based resistive switching devices(memristors) have gained a lot of attention. Here we report the resistive switching behavior of nitrogen-doped graphene quantum dots/Polyvinyl alcohol (N-GQDs/PVA) degradable nanocomposite thin film with different weight percentages (wt.%) of N-GQDs. The memristor device was fabricated by a simple spin coating technique. It was found that 1 wt% N-GQDs/PVA device shows a prominent resistive switching phenomenon with good cyclic stability, high on/off ratio of ~102 and retention time of ∼104 s. From a detailed experimental study of band structure, we conclude that memristive behavior originates from the space charge controlled conduction (SCLC) mechanism. Further transient property of built memristive device was studied. Within three minutes of being submerged in distilled water, the fabricated memory device was destroyed. This phenomenon facilitates the usage of fabricated memristor devices to develop memory devices for military and security purposes.
A study of turbulent filaments in the edge plasma of the Wendelstein 7-X stellarator
Authors: Buzás A. , Kocsis G. , Biedermann C. , Cseh G. , Szepesi T. , Szűcs M. ,   the W7-X Team
Published: March 2024

Abstract
Filaments are studied by examining fast camera images on the Wendelstein 7-X stellarator. Fast cameras offer a unique perspective, revealing the complex 3D structure of filaments in the entire poloidal cross-section of the plasma. By correlating individual pixels, their location, shape, and movement are analyzed in standard and high-ι configurations. The presence of filaments is not uniform poloidally around. The number of active areas matches the number of magnetic islands in both configurations. Filaments are found to extend to multiple toroidal turns in standard configuration. No time delay is observed between the different toroidal sections. Such behavior is not seen in high-ι configuration. Filaments are observed within and without the edge shear layer, indicated by the direction of their poloidal rotation. Inside the shear layer, their velocity scatters around 1.25 km s−1, accompanied by a lifetime between 80 and 120 µs. Outside, their velocity shows greater absolute values and variance, but still in a few km s−1 range. The similarities and differences between the two configurations are discussed and compared to previous results.
Classical stochastic approach to quantum mechanics and quantum thermodynamics
Authors: Oliveira Mário J de 
Published: March 2024

Abstract
We derive the equations of quantum mechanics and quantum thermodynamics from the assumption that a quantum system can be described by an underlying classical system of particles. Each component φ

j
of the wave vector is understood as a stochastic complex variable whose real and imaginary parts are proportional to the coordinate and momentum associated with a degree of freedom of the underlying classical system. From the classical stochastic equations of motion, we derive a general equation for the covariance matrix of the wave vector, which turns out to be of the Lindblad type. When the noise changes only the phase of φ

j
, the Schrödinger and the quantum Liouville equations are obtained. The component ψ

j
of the wave vector obeying the Schrödinger equation is related to the stochastic wave vector by

|

ψ
j

|

2

=
⟨

|

ϕ
j

|

2

⟩

.
Classroom architectural design evolution: Acoustic evaluation of public-school classrooms in Malaysia
Authors: Razali A W , Che Din N , Yahya M N , Sulaiman R 
Published: March 2024

Abstract
Education transformation greatly emphasises curriculum modification to produce impactful future generations and yet often disregards the impact of classroom design in achieving desired education outcomes. The prioritisation of optimal acoustic quality in classroom design is crucial due to the inherent reliance on auditory abilities in the process of teaching and learning. Therefore, a comprehensive acoustic standard guideline for classrooms, ANSI Standard 12.60, was launched in 2002 and adopted for school classrooms in the USA. However, the scenario might be different in Malaysia, as there is no acoustic standard guideline established. Therefore, this study seeks to identify the actual acoustic conditions of classrooms that were constructed in the post-independence era. On-site acoustic measurements were performed to evaluate significant acoustic parameters, including reverberation time (RT), background noise level (BNL), speech transmission index (STI), and sound pressure level (SPL). The findings revealed that the RT for both classrooms was within the recommended value, while the BNL and STI of both classrooms failed to comply with the established recommended guidelines. In a similar vein, these findings translate the degree of awareness among the education institutions and construction sector of the importance of classroom acoustics in providing a better learning experience among students.
A Study of The Sound Absorption Properties of Layered Composites on Chicken Feather Fibers
Authors: Artayani Meldawati , Kusno Asniawaty , Mulyadi Rosady , Jamala Nurul 
Published: March 2024

Abstract
Biocomposites made from animal fibers have now been developed into alternative materials for room acoustics. This study presents a material study on the sound absorption ability of chicken feather fiber composites made experimentally by layered methods of various matrix compositions. Testing the absorbability of material sound using an Impedance tube or Kundt tube method (ISO 10543-2), at a frequency of 200 Hz to 1600 Hz (Medium Sounds). The results revealed that chicken feather fiber composites (CFFC) performed well in sound absorption when using a matrix made from starch. Chicken feather fiber composites (CFFC) perform well in the medium to high-frequency range and can be recommended as indoor sound-absorbing materials.
Monitoring of cooling tower water pumps using Arduino data acquisition device
Authors: Lee Zhiyung Joshua , Anas Md Rezali Khairil , As’arry Azizan 
Published: March 2024

Abstract
Investing equipment to monitor the condition of assets through vibration can be expensive. Recent development of alternative microcontrollers has enabled researchers to study its potential to replace expensive equipment for monitoring purposes. This study aimed to investigate the potential of alternative microcontrollers to be used as devices to monitor the condition of water pumps. An internet of things (IoT) device was developed that can measure vibration and be applied to monitor water pumps. The vibration data was obtained when the pumps were operated and sent to the condition-based monitoring system (CBMS) database via Wi-Fi network. The vibration data was observed, and current and future condition was identified through vibration analysis.
Z-Freq Hybrid: Signal Analysis Based Car Air Conditioning Compressor Monitoring Technique Using Accelerometer and Piezo-Film Sensor
Authors: Othman MNB. , Nuawi M.Z. , Ngatiman N.A. , Yusri M.Y. 
Published: March 2024

Abstract
The efficiency and performance of car air conditioning systems rely heavily on adequately functioning the compressor and its associated components. There are many ways to detect an unfunctioning compressor and one of them is through vibration. Disfunctioning compressors could lead to discomfort, fatigue, stress and fogging windows, especially in long-distance driving. Thus, researchers focus on detecting the nonfunctioning car air conditioning compressor early. This paper introduced a new enhanced statistical method, namely Z-freq Hybrid. Z-freq Hybrid was based on a Z-notch frequency domain filter with a combination input of two different types of sensors introduced to detect functioning compressors. Data were recorded at various compressor speeds using an accelerometer and piezo film sensor with Signal Express 2015 software. The acceleration (m/s2) and voltage (mV) data were analyzed to find the combination degree of scattering data in a Z-freq Hybrid chart. The analyzed data show that the Z-freq Hybrid coefficient increases as the compressor’s speed increases. Then, the value dropped significantly when the compressor was dysfunctional. In conclusion, a Z-freq Hybrid can be employed to detect abnormalities and irregular vibration patterns, which may indicate the impending failure of a compressor.
Research on intelligent control system of permanent magnet motor for high-speed flywheel energy storage system based on deep learning
Authors: Kang Changfeng , Li Keyan 
Published: March 2024

Abstract
With the continuous development of society, more and more people pay attention to energy issues, and the realization of energy storage has become a hot research direction today. Despite advancements, the control system of the high-speed flywheel energy storage system’s permanent magnet motor still encounters issues in effectively regulating the magnetic suspension bearing and motor speed. In addressing this issue, a technical solution involves the implementation of an intelligent control system for the high-speed flywheel energy storage system’s permanent magnet motor, utilizing deep learning principles. This innovative approach employs deep neural networks to model, optimize, and regulate the flywheel energy storage system. The essence of flywheel energy storage lies in the conversion of electrical energy into mechanical energy, followed by its reconversion into electrical energy during output. It has the advantages of high energy density, high power density, long cycle life, fast charging and discharging, maintenance-free and environmental protection. A permanent magnet motor is a motor that uses permanent magnets to generate a magnetic field. It has the characteristics of high efficiency, high power density, and low rotor loss. It remains the most widely utilized motor in flywheel energy storage systems. An intelligent control system is characterized by its use of artificial intelligence technology to adapt, self-learn, and self-organize complex systems. This system is distinguished by its robust nonlinear processing capabilities and resilience to faults. The high-speed flywheel energy storage system permanent magnet motor intelligent control system based on deep learning can improve the performance, efficiency and reliability of the flywheel energy storage system, reduce costs and risks, and is suitable for electric vehicles, rail transit, power grid frequency regulation and other fields. In this paper, the convolutional neural network and PSO algorithm are used to obtain the PSNN neural network structure to predict the speed of the motor, so as to achieve its control. And the reliability of the structure is verified by experiments.
Research on mine ventilation network solution and collaborative control technology
Authors: Chang Weifeng , He Min 
Published: March 2024

Abstract
With the development of intelligent ventilation system in domestic coal mines, the demand for ventilation network solving software is becoming more and more intense. Aiming at the problem that the traditional ventilation network solution algorithm is difficult to meet the increasingly complex ventilation system solution needs of domestic mines, and the lack of functions of the collaborative control of ventilation equipment and facilities, this paper uses theoretical analysis combined with mine reality to study the relevant theories and algorithms in the field of ventilation network solution and collaborative control at home and abroad, and optimizes the network solution and collaborative control related algorithms such as iterative calculation of ventilation network, identification and stability analysis of angle-linked wind network, natural wind pressure calculation, local fan collaborative control, and collaborative control of damper and wind window. Provide theoretical guidance for future network solution software development.
Design and Simulation of Hydraulic Integrated Valve based on SLM Technology
Authors: Liang Keshan , Tang Li , Zhang Qingbiao , Cao Yujun 
Published: March 2024

Abstract
Intelligent robots put forward the need for miniaturization and integration of hydraulic systems. Based on SLM technology, this paper designs a hydraulic integrated valve, integrates flow channels, adapters and other facilities on the valve body, replaces the original linear movement scheme with a rotating spool scheme, studies parameters such as runner diameter and minimum wall thickness, and simulates and analyzes the flow performance of liquid when the main control runner and a single valve work. The results show that the integrated valve has the advantages of light weight, small size and good performance.
Effects of Pore Distribution on Mechanical Properties of Selective Laser Melted Inconel-718
Authors: Gao Kairan , Liu Yue 
Published: March 2024

Abstract
In recent years, Selective Laser Melting (SLM) technology has made significant advancements, offering high precision and near-net shaping capabilities that are widely applicable in aerospace, mechanical, and electrical industries. Inconel-718, known for its exceptional high-temperature corrosion resistance, fatigue endurance, and wear resistance, has found extensive use in aerospace applications such as gas turbine disks, rocket engines, and spacecraft components. However, the presence of porous defects in SLM-manufactured metal parts is inevitable, and these pores significantly affect the mechanical properties of the materials. While contemporary studies have focused on the influence of individual or a limited number of pores, the impact of pore distribution has been largely overlooked. This oversight leads to reduced prediction accuracy and hampers the practical implementation of SLM technology. Therefore, this paper specifically investigates the impact of pore distribution on the performance of SLM-manufactured metal materials, using Inconel-718 as an example. The study establishes simulation experiment models to explore diverse pore distribution patterns, including spatial position distribution models and spatial quantity distribution models. The Finite Element Method (FEM) model utilizes the Johnson-Cook constitutive model, and the pores are equivalently modelled as two-dimensional plane ellipses. Uniaxial tension simulations are performed to analyze the mechanical behavior of the materials. The results demonstrate that the mechanical properties of SLM-fabricated Inconel-718 are significantly influenced by the spatial position distribution, spatial quantity distribution, and spatial orientation distribution of the pores. Under different spatial position distributions, materials with more pores parallel to the stretching direction exhibit poorer mechanical performance due to earlier and more significant stress concentration. Under different spatial quantity distributions, materials with a higher pore density and smaller pore spacing show worse mechanical performance due to earlier and more severe stress concentration. Under different spatial orientation distributions, materials with a larger angle between the pore’s major axis and the stretching direction exhibit worse mechanical performance due to enhanced stress and earlier and more severe stress concentration. Overall, this study highlights the importance of considering pore distribution in SLM-manufactured metal materials, providing insights into the impact of spatial position, quantity, and orientation distributions on the mechanical properties of Inconel-718.