Journal of Mechanical Engineering, Automation and Control Systems
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A k-kNN miscalibrated current transformer identification method based on line topology for distribution networks
Full text linkThe 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
Exercita Rim – physical exercise protocol with blood flow restriction for people with chronic kidney disease on conservative treatment
Full text linkPhysical exercise promotes benefits for people with chronic kidney disease, but little has been investigated on the effects of strength training using the blood flow restriction method. The objective is to present the intervention protocol with strength physical exercise associated with the RFS method for people with stage 3 CKD and to report the benefits in the hemodynamic scope and personal satisfaction after 4 weeks of exposure to physical exercise. The study is an intervention protocol proposed to be developed over 12 weeks, with 9 exercises, in people with CKD-3, on 3 days a week lasting 50 minutes. The participants were divided into groups with low load, with high load and group with blood flow restriction and hemodynamic variables (blood pressure and heart rate) and affective satisfaction in relation to the proposed exercise were measured. Forty people aged 58±8.9 years were recruited, of which 30 participated in the intervention. Regarding satisfaction, the high-load group presented better results (2.8 to 3.5) (p= 0.035); and for blood pressure, the blood flow restriction group showed significance in systolic pressure (p= 0.034). It is concluded that after 4 weeks of intervention with a strength training protocol aimed at blood flow restriction, there are trends of improvements in systolic blood pressure levels, and affective sensations were improved after the end of the exercise sessions
Pattern recognition of acoustic emission signals by Q235 steel corrosion in marine environment
Full text linkTo overcome the limitations of traditional monitoring methods, which are restricted to periodic inspections, this study proposes a real-time method for identifying metal corrosion damage patterns to monitor the condition of Q235 steel corrosion based on acoustic emission (AE). Firstly, AE technology was utilized to monitor the corrosion process of Q235 steel plates in simulated industrial marine environment in real-time. Wavelet packet energy spectrum coefficients, closely related to the damage mechanism, were extracted from the acquired signals. A feature matrix was then constructed using principal component analysis (PCA) to eliminate redundant information and enhance computational efficiency. The K-means clustering algorithm was then applied to classify the AE signals into three classifications: the signals of mode 1 correspond to bubble rupture, the signals of mode 2 to pit growth and expansion, and the signals of mode 3 to the detachment of corrosion products and oxide film rupture. A damage pattern recognition model based on a convolutional neural network (CNN) was developed, enabling the real-time recognition of other unknown AE signals generating during the corrosion process of Q235 steel, and it exhibited satisfactory performance in accurately identifying corrosion-related acoustic emission patterns
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
Kinematic and force analysis of a scissor lift mechanism
Full text linkA scissor lift design was developed with a load capacity of 100 kg and a lifting height of 1 m. The platform lifting mechanism is actuated by a traction electric motor via rollers moving along the guides. A calculation model of the scissor lift was created, resulting in a statically indeterminate system. Support reactions and an actuating force were determined depending on the platform lifting height. The analytical results showed that the actuating force increases nonlinearly during platform lifting, ranging from 1.674 kN to 6.45 kN, while the actuator rod stroke is 441 mm. Similarly, the simulation conducted using SolidWorks Motion yielded the actuating force in the range of 1.62 kN-6.5 kN and the rod stroke of 443.5 mm. The study established the patterns of variation of the main kinematic and force parameters of the scissor lift, which exhibit nonlinear characteristics. A piecewise linear variation of the actuating force was synthesized to ensure a trapezoidal motion profile of the platform. This type of motion profile was selected to provide comfortable and safe movement for people, particularly those with disabilities. The strength of the main structural elements of the scissor lift, namely levers, traction crossbar, guides and rollers, was ensured
Analysis on the influence of blade pitch angle on dynamic characteristics of the rotor system
Full text linkAt present, few studies focus on variable-pitch fans for small-to-medium turbofan engines, with most relying on hydraulic actuation that fails to meet strict environmental and efficiency demands. This paper analyzes an electrically actuated lead-screw servo-motor-driven variable-pitch fan rotor: at 1×10⁷ N/m support stiffness, the first critical speed exceeds the operational range and pitch angle’s influence is negligible, peak unbalance response is 1.22×10⁻⁶ m linearly decreasing with pitch angle, and vibration analysis avoids resonance. Results confirm the electric pitch-change concept’s feasibility
Dynamic performance analysis of 1000 MW double reheat steam turbine foundation
Full text linkIn recent years, power equipment has been developing towards low-carbon, high-efficiency, and green environmental protection. The double reheat unit has been increasingly employed in power plants due to its advantages of low energy consumption and less pollution. As a core component of power plants, the dynamic performance analysis of the steam turbine foundation is essential for ensuring the overall safety of double reheat unit. For this reason, the dynamic performance of a steam turbine foundation is investigated based on the engineering background of frame-type reinforced concrete foundations of 1000 MW double reheat steam turbine set in a power plant. The solid finite element model of the steam turbine foundation is first established by using ANSYS software, along with a detailed description of foundation information and modelling methodology. Subsequently, the dynamic characteristic and response analyses of the steam turbine foundation are performed to evaluate its dynamic performance, respectively. The results indicate that the 1000 MW steam turbine foundation demonstrates satisfactory dynamic performance. Within the operating speed range, the transverse, longitudinal, and vertical vibration displacements of the foundation bearings and columns remain below 20 μm, while the vibration velocity does not exceed 3.8 mm/s, both of which comply with relevant specifications. Moreover, enhancing the stiffness of the fifth and sixth beams, along with increasing the cross-sectional area of columns C3 and C4 on the steam turbine foundation, should be considered to mitigate its vibration responses and thus improve its dynamic performance. The research findings can serve as a reference for the type selection and optimization design of 1000 MW double reheat steam turbine foundations
Vibration and noise performance analysis and optimal design of V-rotor in permanent magnet synchronous motor: a new strategy for high efficiency and low noise
Full text linkInterior Permanent magnet synchronous motors (IPMSMs) have become the preferred powertrain solution for electric vehicles due to their exceptional performance characteristics. However, the high-frequency electromagnetic noise generated during motor operation poses a significant challenge to occupant comfort within the vehicle. This study provides a comprehensive analysis of the electromagnetic forces, modal characteristics, and vibration noise for a 12-pole, 36-slot IPMSM, incorporating theoretical and simulation-based approaches as well as modal tests. By innovatively combining orthogonal experimental design with nonparametric regression techniques, a response surface model is developed to accurately characterize and optimize the radial electromagnetic force harmonics of the motor. The optimization results reveal a significant 37.7 % reduction in the motor’s surface vibration velocity and an 8.5 % decrease in peak noise levels, successfully meeting the engineering objectives for vibration and noise attenuation. This study not only contributes to the advancement of noise control technologies in electric vehicle power systems but also provides novel insights and methodologies for motor design, offering significant practical value and engineering relevance
Study on the variation mechanism of non-linear stiffness of rubber O-ring
Full text linkO-ring dampers can be used as vibration-damping elements for short-life, low-cost engines, and the selection of a suitable rubber superelastic-viscoelastic ontological model to study their stiffness and damping is an important prerequisite for determining their vibration-damping characteristics. The superelastic-viscoelastic constitutive model consists of two models, superelastic and viscoelastic, in which the superelastic model reflects the static characteristics of the O-ring. Therefore, it is the basis of the study of dynamic characteristics to carry out the research on the static stiffness of the O-ring and to select an accurate superelastic model to describe its deformation and recovery characteristics under different working conditions. Based on the fact that the O-ring is in a small deformation range in the damper and the applicability of finite element simulation, the Mooney-Rivilin superelastic constitutive model is selected in this paper. Establish a three-dimensional finite element model of the O-ring damper, focusing on the analysis of the effect of temperature on the O-ring material properties and damper structure, to reveal the mechanism of non-linear stiffness change of the O-ring damper. At the same time, the accuracy of the hyperelastic model is verified by the test method, which lays a foundation for the study of the dynamic stiffness and damping characteristics of the O-ring. The results show that in the pre-compression state, there is a large contact pressure between the O-ring and the inner and outer rings of the damper. The contact pressure increases linearly during the compression process, and the stiffness of the O-ring changes linearly. In the non-pre-compression state, the contact pressure is 0, the contact pressure increases nonlinearly during the compression process, and the stiffness of the O-ring shows obvious nonlinear characteristics. In addition, the static stiffness of the O-ring increases with the increase of pre-compression amount, increases with the increase of material hardness, and decreases with the increase of temperature. The above research provides a reference for selecting the appropriate O-ring material size and installation conditions in the project to ensure that the O-ring can effectively withstand pressure during use
Acoustic detection of fan blade faults based on dynamic Cauchy swarm algorithm to optimize support vector machine
Full text linkFan blades operate in outdoor environments, where the detection of sound signals is susceptible to interference from background noise such as random loads, wind speed, rainwater, and other ambient noise. Therefore, this article proposes an acoustic detection method for wind turbine blade faults based on a dynamic Cauchy bee colony algorithm-optimized support vector machine. First, the signal is preprocessed using a Butterworth bandpass filter, and the full frequency band is divided into sub-bands using the octave band feature extraction method. Based on frequency domain analysis, the natural frequency offset of the blade is determined. Next, the dynamic Cauchy bee colony algorithm is applied to optimize support vector machine parameters, while moving average and bandpass filtering are used to smooth the noise power curve and extract impeller speed information. The experimental results show that the proposed method converges in fitness value after 22 iterations, with a detection time of only 6.8 seconds and small fluctuations in impeller speed amplitude. In terms of classification performance, the accuracy of detecting normal samples is 0.95, the recall rate is 0.96, and the F1 score is 0.95. The method demonstrates high prediction accuracy and stability for various types of fault samples and can be reliably applied to the acoustic detection of wind turbine blade faults