Maintenance, Reliability and Condition Monitoring
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Methods to increase the throughput and carrying capacity of the “Angren-Pop” railway section in line with expected transit freight flows from the “China-Uzbekistan-Kyrgyzstan” railway project
Full text linkThe development of the “China-Kyrgyzstan-Uzbekistan” railway (hereinafter referred to as the CKU) can be cited as a promising project to increase transit cargo flows in our country. In organizing the uninterrupted transportation of transit freight flows planned to pass through the territory of our country as a result of the implementation of this project, the “Angren-Pop” railway section, which includes the 19.2 km long “Kamchik” tunnel, is of great importance. This article analyzes the impact of the development of the CKU railway on the throughput and carrying capacities of the “Angren-Pop” railway section. The current maximum freight capacity of the “Angren-Pop” railway section has been studied. The results show that this section is not capable of handling the expected volume of transit cargo. This substantiated the need to find solutions for effectively increasing the carrying capacity of the section while ensuring an economically rational balance. Methods for effectively increasing the carrying capacity of the section are recommended, including the systematic implementation of measures such as increasing the standard weight of freight trains, raising the operating speed on the section, and using electric locomotives with high tractive power
Key construction technologies for in-situ reconstruction of a continuous girder bridge onto a steel truss arch bridge
Full text linkThis study explains the challenges of reconstructing a continuous beam bridge and its effects on the performance of adjacent structures. Combined in-situ demolition and modification of continuous beam bridges with the new construction of steel truss arch bridges, an integrated construction method is established. Taking a bridge as a construction platform, the temporary fixation technology is used for the tie beam hook. Various erection techniques of the bridge and tie beam construction support frame, as well as the construction techniques of Truss steel arch and wind bracing are studied and explored. In addition, the method of simultaneous disassembly and construction methods of crossbeams are also studied. Finally, a new technology is developed to reconstruct Truss arch bridge on continuous beam bridges
Research progress on 3D printed geopolymer materials
Full text linkThe integration of 3D printing technology with geopolymer materials offers a sustainable alternative to conventional construction methods, significantly reducing CO2 emissions. However, challenges such as rapid setting, limited workability, and weak interlayer bonding limit their broader application. This review summarizes recent progress in 3D printed geopolymer composites, focusing on materials selection, rheological optimization, buildability, and mechanical performance enhancement. Strategies including the use of rheology modifiers, fiber reinforcements, nano-additives, and process optimization have shown promise in improving printability and structural performance. Remaining challenges, such as balancing setting time and printability and enhancing interlayer adhesion, are also discussed. Future research directions are proposed to further advance the development of high-performance, low-carbon geopolymer 3D printing materials for sustainable construction
Use of fragility curves to assess the seismic vulnerability of soft rock tunnels: a review
Full text linkDue to their distinct geotechnical and structural features, soft rock tunnels pose serious issues because of their seismic sensitivity. These tunnels, often constructed in formations with lower shear strength and higher deformability, are particularly susceptible to damage during earthquakes. Fragility curves, which graphically represent the probability that a structure may sustain damage up to or beyond a particular threshold as a function of seismic intensity, are essential tools for evaluating the seismic resilience of these infrastructures. This research looks closely at the use of fragility curves to assess the seismic vulnerability of soft rock tunnels. Exploring the fundamental concepts and methodologies involved in constructing fragility curves, including seismic hazard analysis, structural modeling, damage state definition, data collection and statistical analysis is looked at first. The review highlighted the integration of soft rock characteristics such as strength and deformation properties into the fragility assessment process. Key developments in the topic are covered such as how machine learning and Bayesian inference might improve the precision and usefulness of fragility curves. The paper identified key findings such as the high sensitivity of fragility curves to geotechnical properties and seismic intensity levels and emphasized the importance of accurate data collection and model calibration. Important gaps in seismic risk evaluations are filled by integrating cutting-edge methodologies, such as Bayesian inference and real-time machine learning models that clarify the seismic behaviour of soft rock tunnels in the real world. For the purpose of strengthening earthquake-resistant infrastructure in earthquake-prone areas, engineers, scholars and policymakers are given practical insights
Research on coupling dynamic characteristics and parameter influence of TBM cutterhead system
Full text linkAs an important system of TBM, the host system bears the impact of unstable load from itself and the strong load of the rock in the geological layer during operation, which causes irregular vibration of the host system, resulting in low tunneling efficiency, and is more likely to cause cutterhead cracking and component damage. To this end, with the help of analysis software such as Matlab and Ansys, the intrinsic characteristics and vibration response of the host system are studied, and the specific parameters of the vibration influencing factors are discussed. The results show that the axial displacement of the center block of the cutterhead is the largest, reaching 0.85 mm, and the longitudinal displacement value is about 2-3 times of the transverse displacement; in the design stage, the mass of the central block should be controlled in the range of 50 %-55 %, and the rest of the cutterhead should be controlled in the range of 12.5 %-13.5 %; the vibration is the smallest under the uniform layout of the gear, the fluctuation of the solid short shaft connection of the motor is relatively stable, and the maximum vibration value does not exceed 3.5e-2 mm
Application of unsupervised identification of dissolved gases in transformer oil based on spin coating film making process
Full text linkAddressing the issues of low efficiency and uneven collection of dissolved gases in transformer oil leading to overfitting and poor performance of identification models, we propose a novel film-making process that integrates Gaussian process and unsupervised pre-classification to enhance the recognition efficiency of dissolved gases in transformer oil. This method not only forms a thinner and more uniform separation layer, significantly improving degassing performance and collection efficiency, but also addresses the problems of insufficient data labeling and sample imbalance by introducing the K-means++ clustering algorithm and pseudo-random integration technology, thereby enhancing model robustness and generalization ability. Moreover, the designed Gaussian Process Multi-Classification (GPMC) method employs probabilistic interpretation for result presentation, which increases the accuracy of fault identification. Experimental results show that under consistent starting conditions, the RCC and ARI indicators of our pre-classification method are close to 0.8, with the test set’s recognition rate exceeding 80 %, while the GPMC method misclassified only 2.4 % of the cases in the 1800-case dataset. These improvements make our method particularly effective for handling uncertainties and imbalances in dissolved gas cases in transformer oil, showcasing its potential for practical applications
Magnetoelastic oscillation of current-carrying plates in an alternating magnetic field
Full text linkModern technological advancements, particularly in micro- and nanoelectronics, aerospace engineering, sensor systems, and robotics, necessitate a deeper understanding of how structural elements behave under various physical influences. One significant and relevant phenomenon is magnetoelastic interaction, which involves how the mechanical behavior of current-carrying elastic bodies is affected not only by external loads but also by internal electromagnetic processes. Current-carrying plates, commonly utilized in micro- and nanoelectronics, respond to external fields by altering their stress-strain states. To accurately model these processes, an integrated approach is required that considers mechanical, electromagnetic, and thermal effects caused by electrical currents. This paper focuses on the mathematical modeling and numerical study of transverse magnetoelastic oscillations in thin current-carrying plates subjected to an alternating magnetic field. The problem is formulated considering electromagnetic interactions, geometric nonlinearity, and external alternating currents. A comprehensive system of equations is developed that includes the equations of motion, Maxwell's equations, and the heat equation with Joule heating sources. For the numerical solution, the finite difference method using the Newmark scheme and discrete orthogonalization techniques are applied. Graphs illustrating stress and strain distributions are presented, and the effects of magnetic field frequency and external current on the system’s behavior are analyzed. This research is vital for designing reliable components in micro- and nano-electronics and aviation
Assessment of the influence of external dynamic factors on force loading of anchor bandage of traction electric motors
Full text linkA methodology has been proposed for calculating the force loading of anchor bandages of locomotive traction electric motors from the action of external dynamic factors, which makes it possible to determine dynamic stresses in each section of the anchor bandage along its entire length, depending on the operating modes of the traction electric motor, taking into account its design features and real operating conditions. It has been established that the most significant influence on the fluctuations of the armature shaft of traction electric motors of diesel locomotives is exerted by dynamic influences from the collision of wheels with joints and unevenness of the rail track, as well as from errors in the manufacture of the serrated broadcast (gears). The supposed economic effect from the creation of new glass bandage designs for the anchors of traction electric motors of diesel locomotives of the 2TE10M series is estimated at approximately 10.92 million soums for one such diesel locomotive. It is recommended to continue these studies in order to develop and justify rational geometric parameters of a new design of the anchor glass bandages of a traction electric motor with increased fatigue strength
Influence of angular speed of tedder on kinematic parameters of linter machine drive
Full text linkThis article investigates the influence of the tedder’s angular speed on the kinematic and power characteristics of the drive system of the 5LP linter machine. The linter machine is a complex technological unit used to remove residual fibres from the surface of cotton seeds. One of the key factors determining linting efficiency is the interaction between the tedder and the seed roller inside the machine’s working chamber. A detailed kinematic and force analysis is presented, taking into account the resistance forces generated by the seed roller during its movement and processing. Particular attention is given to the development of a calculation model that describes the interaction between the tedder blades and the seed roller. In this model, each blade is treated as a cantilever beam subjected to variable loads resulting from the non-uniform mass and density distribution of the seed material. The analysis demonstrates that variations in the mass and density of the seed roller significantly affect the load transmitted to the drive and the stability of the saw cylinder. The obtained results enable more accurate selection of drive parameters and optimisation of the operating modes of the linter machine. These findings are crucial for improving the productivity and reliability of the equipment, as well as for accounting for both transient and steady-state operating conditions in real industrial environments
Advancing industrial gas turbine field performance testing: a review of procedures and key considerations with emerging technologies
Full text linkThis review explores the possibility of enhancing the efficiency and accuracy of Industrial Gas turbine Performance testing by critically assessing the traditional methods, their limitations, and how modern technologies can be used to complement the existing traditional testing approaches, optimize data acquisition, and predict operational failures. A systematic and comprehensive search strategy was employed to identify relevant academic and industry literature. Studies on traditional testing practices were reviewed to highlight their constraints, while researches involving the application of emerging technologies for performance diagnostics were also reviewed to illustrate their benefits. Findings show that measured data such as turbine inlet temperature, compressor pressure ratio, exhaust temperature, fuel flow, shaft speed, and vibration remain essential for both traditional and AI-enhanced methods. These parameters, typically obtained through standardized testing procedures, provide the foundational input for AI models such as machine learning algorithms and digital twins. The study revealed that AI technologies thrive in data-rich, repeatable environments by enhancing processes like instrumentation, data logging, and normalization. The study also revealed that machine learning, deep learning, artificial neural networks, and digital twins can be used for more effective planning, reduce redundant testing, and mitigate delays caused by variable factors like weather or load conditions