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Analysis of the influence of vibration phenomena in pump systems on electrical energy consumption and operational efficiency
Get PDFDespite the long-standing recognition of vibration phenomena as a critical factor affecting both mechanical reliability and energy performance, yet their influence on electrical energy consumption remains insufficiently quantified. Excessive vibration, originating from rotor imbalance, shaft misalignment, bearing wear, and hydraulic instabilities, can result not only in accelerated component degradation but also in significant increases in energy demand and reductions in hydraulic efficiency. Understanding the quantitative relationship between vibration intensity and pump energy performance is therefore essential for both predictive maintenance strategies and energy efficiency improvements in pumping systems. This paper presents an experimental investigation of the effect of vibration on the electrical energy consumption and operational efficiency of centrifugal pumps. Five industrial pump types, with rated powers ranging from 15 to 75 kW and capacities from 100 to 320 m3/h, were tested under controlled conditions. Measurements were carried out using UT310A vibration testers, an ultrasonic flow meter, and a Fluke 1777 Power Quality Analyzer. Vibration signals, volumetric flow rates, pressure heads, and three-phase electrical parameters were simultaneously recorded under partial load, nominal load, and overload conditions. Hydraulic power and efficiency were then calculated, while statistical analyses-including correlation and regression models-were applied to determine the relationship between vibration intensity and electrical performance. The results revealed a strong positive correlation between increasing vibration levels and higher electrical energy demand. In particular, RMS vibration acceleration was found to be a reliable predictor of additional energy losses, while efficiency was observed to decrease as vibration intensity increased. These findings not only confirm the detrimental effect of mechanical instability on energy consumption but also provide a methodological framework for integrating vibration monitoring into energy management practices. By bridging the gap between mechanical diagnostics and energy performance analysis, the study contributes new insights that can support the development of predictive maintenance systems, improve pump reliability, and promote more sustainable operation of pumping stations
The technology of non-stop passage of high-speed passenger and freight trains on double-track sections and its impact on operational performance
Get PDFCurrently, a mixed system of high-speed passenger and freight trains has been implemented on the railways of Uzbekistan. During the movement of high-speed passenger trains on double-track main line sections, the movement of freight trains at all stations and segments is temporarily suspended for a specific period. From this perspective, in the present time, the suspension of freight train traffic is leading to numerous technical and economic expenses. In this article, based on experimental runs using the technology of passing freight trains without stopping, train movement schedules have been drawn up, and train operations have been organized without unnecessary stops. This research paper provides a restructured overview of the introduction and practical implementation of non-stop train operation technology for double-track railway lines where both high-speed passenger and freight trains operate simultaneously. Using real operational schedules, comparative experimental charts were developed to evaluate the new approach. The outcomes of this analysis demonstrate how the proposed technology influences efficiency and key performance indicators of train movements. A regression-based analytical model was also constructed to determine the relation between freight train waiting time and average section speed, ensuring reliability through statistical verification. Furthermore, the application of innovative solutions and technologies mentioned in this article to sections of high-speed highways creates an opportunity to increase transport transit potential and improve economic indicators
Towards the efficiency research of the working process of locomotives diesel under operating conditions
Get PDFA method is proposed for quantitative assessment and justification of the criterion of the rationing indicators of external and boost air temperature factors on the qualitative component of the working process of two-stroke supercharged diesel engines under various load conditions of the traction power plant of operating diesel locomotives. The results of the study were obtained in the numerical values and graphs, as well as analytical dependencies (equations) designed to substantiate the parameters under study, including their average values under different operating mode diesel and ambient temperatures. These studies are recommended to continue with the aim of studying the intensity of the dynamics of the decrease or increase in the relative filling coefficients of the 10D100 diesel cylinders with air and developing a methodology for predicting the criterion of the influence of the rationing of boost indicators and outside (external) air on the operating process of diesel locomotives diesels
Erratum: Vibrodiagnostics and dynamic operation of reinforced concrete sleepers under the influence of moving load
Get PDFResearch on bearing equipment fault diagnoses via SAWOA-LSTM
Get PDFTo address the current low fault diagnosis accuracy problem for bearing equipment, and improve the detection methods, in this paper a sine-adapted whale optimization algorithm (SAWOA)-based optimization of a long short-term memory (LSTM) network is proposed as the equipment fault diagnosis method (SAWOA-LSTM). First, an optimization strategy based on sinusoidal population initialization and adaptive optimization is proposed for the whale optimization algorithm, which has the two drawbacks of slow convergence and easily falling into a local optimum. Second, to improve the accuracy and efficiency of fault diagnoses, the SAWOA is used to optimize the number of hidden units and the learning rate parameter of the LSTM. Compared with ACO-, PSO-, and WOA-based LSTM models, the proposed method improves diagnostic accuracy by 14.17 %, 15.03 %, and 4.32 %, respectively. In tests on 50 bearing samples, SAWOA-LSTM further improves accuracy for RBD, IRA, and ORD by 1.08 %, 1.62 %, and 1.10 %, respectively. Our algorithm provides an innovative solution for the health management of complex industrial bearing equipment
The meshing characteristics of planetary gear transmission system considering the effects of load and system parameters
Get PDFIn order to investigate the effects of load and system parameters on the planetary gear system, this paper develops a nonlinear time-varying dynamic model of a 12-degree-of-freedom planetary gear system. The model is employed to analyze the influence of load and system parameter variations on the meshing force, kinetic energy, strain energy, and load-sharing coefficient. The results demonstrate that the unbalance exacerbates tooth collision and increase the peak meshing force. Increasing the load will reduce the tooth collision and enhances the load sharing coefficient of the system. Decreasing the tooth side clearance, the unilateral tooth collision will be transformed into bilateral collision and mitigates the fluctuation of the meshing force. Additionally, increasing the transmission error amplifies the magnitude and fluctuation of the strain energy but reduces the system’s load-sharing coefficient
Improving summer outdoor comfort in metropolitan park: a data-driven approach using AI, experimental and design analysis
Get PDFThis study aims to address the growing urban heat challenges by exploring the application of AI-driven simulations to improve outdoor thermal comfort and air quality in urban parks. The primary goal was to optimize park designs using advanced AI technologies and data analysis, improving the quality of public green spaces. A highly accurate AI model was employed, with performance metrics including RMSE (3.68 °C), MAPE (6.50 %), and a Pearson Correlation Coefficient of 0.982, to evaluate key environmental parameters such as temperature, wind speed, and thermal radiation. These assessments served as the foundation for design optimization through the integration of AI and Computational Fluid Dynamics (CFD) modeling. Innovative design improvements, such as enhanced shading structures, strategic vegetation placement, and refined material selection, resulted in a 15 % reduction in thermal radiation, a 1 m/s increase in wind speed, and a decrease in PM2.5 and PM10 concentrations by 12 % and 15 %, respectively. These changes led to increased pedestrian comfort, improved health outcomes, and a 20 % rise in park usage. Post-optimization analysis further demonstrated a 25 % reduction in thermal radiation and a 10 % improvement in the Air Quality Index (AQI). Furthermore, resilience testing for short-term climate changes indicated that these design improvements would remain effective for at least three years, confirming the robustness and long-term sustainability of the AI-enhanced strategies. This research highlights the potential of integrating AI technologies in urban park design, offering valuable insights into creating sustainable, user-centered green spaces. By combining real-world environmental data with AI-driven optimization, the study emphasizes the importance of interdisciplinary approaches in enhancing the livability and resilience of urban environments
Design and simulation of a cable-driven elbow rehabilitation device
Get PDFWith the increasing prevalence of neurological and musculoskeletal disorders, the demand for effective rehabilitation technologies has grown. This study presents the AlmatyExoElbow system with a cable-driven exoskeleton for elbow rehabilitation with two degrees of freedom. The device was designed in SolidWorks CAD and tested in SolidWorks Motion to evaluate flexion/extension and pronation/supination trajectories. The design is simple, adaptable, and cost-effective, making it a promising candidate for future clinical integration and personalized therapy
Enhancing PLL performance in weak grids: a comparative analysis of backward and bilinear ADC with SOGI-PLL
Get PDFMost rectifiers using AC grid voltage assume that the voltage is ideal and has no distortion. However, in high-power systems such as water electrolysis, the grid voltage can be distorted. This situation is called a weak grid. In weak grids, the switching of rectifiers causes voltage distortion. Distorted voltage causes phase errors during observation, so it is important to measure voltage without distortion. There are two common methods to reduce errors during observation. One is using a hardware Low-Pass Filter (LPF) to reduce high-frequency switching distortion. The other is using a Second-Order Generalized Integrator (SOGI) Phase-Locked Loop (PLL) to separate the distorted component. Both methods are commonly used, but their performance changes depending on how they are applied. This paper compares the distortion reduction of the hardware LPF and the error caused by the digital method of the SOGI-PLL. Simulation results show that the hardware LPF reduces distortion by about 75 %, and the SOGI-PLL can have up to 6.7 % error depending on the digital method. These results are verified through PSIM simulation
A k-kNN miscalibrated current transformer identification method based on line topology for distribution networks
Get PDFThe operational duration and environmental factors associated with current transformers (CTs) in distribution networks makes them prone to measurement miscalibration during their operation. To address this, a kernel k-nearest neighbor (k-kNN) miscalibrated CT identification method based on line topology is proposed. This method relies on the composite characteristics of load currents specific to certain line topologies. High-precision secondary-side CT current data provided by the current acquisition devices in the feeder area are used to construct a multiple linear regression model. The multiple linear regression model is established in the complex domain, and indirectly assesses the measurement status of the current transformers by analyzing the complex coefficients. Building upon the kNN identification algorithm, a kernel function is introduced to map low-dimensional distance feature vectors into a higher-dimensional feature space where linear separability is significantly enhanced, thus improving the accuracy with which abnormal coefficients can be detected in the multiple linear regression model. Experimental simulations and field application scenarios demonstrate that the proposed method significantly outperforms traditional kNN algorithms in terms of classification performance. Specifically, there is an increase of 12.0 % in the F1 score, a rise of 13.3 % in accuracy, and an improvement of 12.0 % in recall. Moreover, in practical engineering applications, the recognition metrics consistently exceed 93 %, which substantiates the effectiveness of the proposed miscalibrated CT identification method