Maintenance, Reliability and Condition Monitoring
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Data analytics-based model for optimizing cationic retarder and acetic acid in polyacrylic yarn dyeing
Full text linkThe study aims to develop a model for optimizing the concentration of solution parameters in the dyeing process of polyacrylic yarns. Specifically, the study examines the use of cationic retaiders and acetic acid, which affect the wavelength as an indicator of yarn color aging. By optimizing these parameters, the objective is to improve the color stability and longevity of dyed polyacrylic yarns. The application of Response Surface Methodology (RSM) encompassed two distinct types of data distribution properties: linear and non-linear. The R-squared (R2) value for the non-linear RSM model was 0.96, compared to 0.86 for the linear RSM model. These results indicate that the model formed based on the non-linear RSM offers superior predictive ability in optimizing solution concentration as a parameter in the polyacrylic yarn dyeing process compared to the linear RSM-based model. In addition to providing practical implications for textile practitioners, this study contributes theoretically by emphasizing the effectiveness of statistical methods such as RSM in manufacturing process analysis
Vibration velocity control of compound wedge-shaped excavation blasting in tunnels under complex environments
Full text linkThis paper explores the optimization of cutout schemes in tunnel excavation and blasting by introducing an improved segmented wedge-shaped blasting method, validated through both numerical simulations and field tests. The numerical simulations use the ANSYS/LS-DYNA fluid-solid coupling algorithm to analyze the damage effects, effective stress distribution, and vibration characteristics of surrounding rock for both the Conventional wedge cut and the segmented wedge cut methods. The results show that segmented wedge cutting significantly enhances the utilization rate of blast holes, reduces the formation of large gravel fragments, and effectively mitigates surrounding rock vibration velocities. In comparison to the Conventional method, the optimized undercut scheme not only increases blasting efficiency but also greatly enhances the rock fragmentation in the undercut area, thereby ensuring tunnel construction safety. The field test results validate the accuracy of the numerical simulations and show that the enhanced scheme holds significant potential for practical application in real-world projects
A novel problem and algorithm for solving permuted cordial labeling of corona product between two graphs
Full text linkThis study has come up with a new application of permuted cordial labeling initiated by two graphs based on their corona product, furthering the cause of a better comprehension of and research into specific types of graphs. The Permuted cordial labeling construction for the corona product of graphs consisting of paths, cycles, second power of a path and second power of cycle graphs may facilitate the consideration of the properties and structures of the graphs. It helps us to study its topological properties, connectivity images, symmetries and other properties
Self-supervised CNN for user behavior analysis on smart meter data
Full text linkSmart meters generate extensive data on individual consumer electricity usage, providing valuable insights that can aid in identifying demographic information and advancing the development of smart grids. Current research has primarily focused on traditional machine learning approaches for this task, with relatively few studies exploring deep learning methods, despite their potential for more accurate and efficient analysis. To address this gap, this paper proposes a self-supervised deep learning approach based on Convolutional Neural Network (CNN) to identify demographic information from smart meter data. The model leverages the Fast Fourier Transform (FFT) to detect frequency cycles within the dataset, which are then used to optimize the sizes of convolutional kernels. This design enhances periodic stability during shallow feature extraction, improving the model’s ability to capture meaningful patterns in the data. Furthermore, the model incorporates a self-supervised pre-training strategy to predict temporal and spatial interactions in load signals, effectively enhancing representation learning without relying on extensive labeled data. This approach ensures the model’s robustness and adaptability to different datasets. Comprehensive experiments were conducted on a publicly available Irish dataset to evaluate the model’s performance. Results demonstrate that the proposed model surpasses a series of state-of-the-art (SOTA) methods, achieving superior performance in demographic information identification. These findings highlight the effectiveness of integrating FFT-based kernel design and self-supervised learning in improving feature extraction and representation learning for smart meter data
Dual-stator ultrasonic motor achieving 2-DOF linear and rotary motion with single-phase excitation
Full text linkThis study proposes a novel dual-stator linear-rotary ultrasonic motor. The piezoelectric ceramic excites both out-of-plane and in-plane vibration modes within the stator. These distinct vibration modes independently drive the slider (rotor), generating reciprocating linear and rotational motions, respectively. Finite element analysis and laser vibrometer-based vibration testing validated the motor's operational principle. The close agreement between simulated and measured resonant frequencies for both vibration modes, with mere discrepancies of 3 % and 4 %, respectively, underscores the accuracy of the stator’s vibrational characteristics. Subsequently, two stators are fabricated and assembled to the ultrasonic motor prototype. Experimental results demonstrate the motor’s impressive performance, achieving a maximum linear velocity of 265 mm/s and a peak rotational speed of 1600 rpm. Furthermore, the motor delivers a maximum thrust force of 0.18 N and a stalling torque of 1.8 mN·m
Improvement of the system for reporting the state of the electrified railway contact line
Full text linkEnsuring the stable and reliable operation of electrified railways requires continuous monitoring of the overhead contact line (CL), whose mechanical displacement under wind loads can lead to interruptions in power transmission, pantograph detachment, and safety hazards. Traditional inspection and monitoring systems are limited in responsiveness and cannot provide real-time information about the dynamic state of the CL. This study presents an improved contact line deviation reporting system based on distributed Signal Processing and Transmission Modules (SPTM) and Signal Reception Modules (SRM) connected through a GSM wireless communication network. Each vibration sensor installed on the catenary wire continuously measures the displacement amplitude, converts the analog signal into digital form, and transmits it to the dispatcher or driver in real time. The developed modules were implemented using microcontrollers with embedded wireless interfaces, allowing autonomous operation powered by solar-assisted batteries and ensuring electromagnetic protection under high-voltage (25 kV) conditions. Field experiments were carried out on an electrified railway test section near the Tashkent depot to evaluate the system’s performance in real environmental conditions – including wind speeds of 5-18 m/s, ambient temperatures from –10 °C to +38 °C, and during snow and rain. The results confirmed stable data transmission up to 1 km distance with signal delay below 0.8 s and detection accuracy above 95 %. The proposed system thus enables real-time monitoring, automatic warning, and high reliability of communication even under harsh weather conditions, significantly improving the safety and efficiency of train operation. The novelty of this work lies in the practical validation of a GSM-based monitoring network for contact line deviation detection that integrates autonomous power supply, environmental robustness, and real-field reliability testing – aspects that are rarely demonstrated in previous studies
Vibration-resistant mixed binders using man-made burnt rocks for transport infrastructure
Full text linkThis study presents the characteristics of man-made wastes, specifically burnt rocks formed by the self-combustion of coal-bearing waste dumps, whose chemical and mineralogical composition depends on the origin of the basin. The aim of this research is to assess the feasibility of using these burnt rocks as components of mixed mineral binders and to evaluate their influence on mechanical and dynamic performance parameters. A comprehensive analysis of their physical, chemical, and structural properties was carried out, demonstrating their compatibility with conventional binder materials. The novelty of this study lies in the first systematic use of locally available burnt rocks (glyage) in vibration-resistant binder compositions for transport infrastructure, expanding the raw material base of construction materials while reducing environmental impact. The developed binders achieved compressive strengths up to 17.6 MPa, sufficient for structural layers of pavement bases and subgrade stabilization. Moreover, these mixed binders can modify the dynamic stiffness and damping behavior of pavement structures under moving vehicle loads, establishing a scientific link between binder composition and vibration control in transport engineering. These results are directly relevant to vibration engineering, as the dynamic stiffness and damping behavior of the developed binders influence vibration propagation and attenuation in transport pavements, ensuring longer service life and reduced noise and deformation under dynamic traffic loads
Die casting mold design of CH367B1 aluminum alloy throttle valve body
Full text linkAs a core component of the automotive engine intake system, the throttle valve body is subjected to long-term engine vibrations. Defects such as gas pores and shrinkage pores in the die-cast part will cause uneven stiffness of the throttle valve body, and thus lead to fatigue failure due to local stress concentration under vibration loads. In this paper, the aluminium alloy was used as the die-casting alloy to design an efficient and simple die-casting mould for CH367B1 aluminium alloy throttle valve body. After measuring the target dimensions and conducting a preliminary analysis of the UG 3D drawing of the part, the size and structure of the valve body were analysed according to the 3D model of the part to select the appropriate parting surface. In the design process, the clamping force, chamber capacity, projected area and other parameters were calculated in order to select a suitable die-casting machine. Part-related dimensions and features were analyzed. Push-out mechanisms, molded parts, guide mechanisms, etc. were designed. The whole set of molds was obtained. Suitable casting systems were designed by calculating and checking references. Mold flow analysis was carried out using ProCAST2021 software. The optimal solution was selected by observing the liquid metal filling process and the distribution of defects, and then calibrated
Erratum: Vibrodiagnostics and dynamic operation of reinforced concrete sleepers under the influence of moving load
Full text linkThe current state and challenges of population mobility in the Republic of Uzbekistan
Full text linkThis article is devoted to the analysis of the public transport systems’ coverage ratio of the central cities of the Republic of Uzbekistan except the capital city. During the research the population density and public transport network have been analysed by comparing the coverage ratio