Maintenance, Reliability and Condition Monitoring
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Experimental analysis of the process of purifying transformer oil from various impurities under the influence of a constant electric field and assessment of mathematically modeled results
Full text linkThis paper has examined the cleaning of transformer oil by using electricity field on a theoretical and practical basis. Scientific analysis was done on the physicochemical characteristics of the oil and the effects of impurities on the electrical insulation characteristics of oil. A mathematical model explaining the movement of impurities under the action of an electric field was constructed on the basis of which, the extent of oil purification was established. According to the carried out theoretical and practical studies, the efficiency of transformer oil purification by constant electric field was tested. Based on the findings of theoretical modeling, the level of oil extraction against mechanical impurities is 40, and the electrical strength of the oil is enhanced by 16 and based on the findings of experiments that are carried out under laboratory conditions, the electrical strength is enhanced by 21. Due to the comparison between theoretical and practical outcomes, the deviation of the electrical strength indicator of the oil does not exceed 5 % and this fact supports the validity of the theoretical model developed. It was also determined that the purification process is affected by environmental factors like temperature, humidity, strength of electric field and composition of the oil. Enhancing oil purified in constant electric field is one of the effective solutions to the enhancement of the reliability of transformers and their service life
Machining parameters optimization in high-speed milling of titanium alloy chips
Full text linkHigh-speed cutting of titanium alloy has the advantages of high processing efficiency, reducing tool wear, and obtaining good surface quality, but there is a lack of research on the influence of chip shape on machining parameter selection mechanism in the cutting process, which hinders the development of high-speed milling quality of titanium alloy. In this paper, the shape of titanium alloy (Ti6Al4V) chips at different speeds and different temperatures were simulated. With the increase of cutting speed, the tool squeezes the workpiece material, causing it to undergo elastic deformation and thereby forming a cutting layer. As the cutting process progresses, the chip gradually takes on a serrated shape, the degree of sawtooth sharpening of chips was analyzed. The formation mechanism of chips and the formation process of sawtooth chips during right-angle cutting were analyzed. The locust optimization algorithm was used to optimize the multi-objective parameters, and it was found that high performance machining effect could be achieved by using large feed speed, radial cutting depth and spindle speed
Improved cubature Kalman filter for vehicle state estimation and measurement
Full text linkThe accurate estimation of vehicle state parameters has a significant impact on the active safety system of automobiles. Accurately obtaining vehicle operating parameters is the foundation and prerequisite for active safety control of vehicles. In response to the limited estimation accuracy of the traditional CKF method, the CKF was extended to fifth-order according to the third-order sphere-radius cubature rule, making it have the accuracy of fifth-order Taylor series expansion. At the same time, singular value decomposition was used instead of traditional Cholesky decomposition to form a fifth-order cubature Kalman filter (SVD-FCKF) estimator for singular value decomposition. Then, the SVD-FCKF was validated using the Carsim and Matlab/Simulink joint simulation platform. Finally, the effectiveness of the proposed method was verified through virtual experiments. The results show that the improved SVD-FCKF estimator can effectively improve the accuracy and stability of vehicle state estimation, with overall estimation performance better than the CKF estimator and has strong adaptability under high and low adhesion coefficient conditions. The research results can provide theoretical support for the active safety research of intelligent vehicles and have practical application value
Design and simulation verification of differential spiral bevel gear transmission based on baja off-road vehicle
Full text linkThe differential spiral bevel gear of Baja off-road vehicle is designed and verified. Based on the competition rules and vehicle transmission parameters, the key geometric parameters of the gear pair are determined, and the three-dimensional model and finite element analysis software are established by UG for static contact analysis and modal analysis. The results show that the maximum contact stress of the tooth surface is 411.4 MPa, and the natural frequency of the gear pair is much higher than the excitation frequency of the system, which can effectively avoid the resonance risk. Through analysis and verification, it meets its application conditions
Study on monitoring the loose bolts of transmission tower by vibration signal
Full text linkThe loose failure of transmission tower bolts may lead to structural instability and safety risks, so effective monitoring methods are crucial to the stable operation of transmission lines. The purpose of this study is to explore the effectiveness of vibration signal technology in monitoring the loose bolts of transmission tower. Based on vibration theory, the principle of bolt loosening of transmission tower is analyzed, vibration signal data is collected by means of vibration exciter and optical fiber vibration sensor, and then the monitoring test of transmission tower loosening fault is carried out. The obtained test results show that the time domain waveform is significantly different before and after excitation, and the wavelength after excitation has a significant mutation, increasing from 1550 nm to 1553 nm, and slowly decreasing to the original wavelength, which also means that the transmission tower bolt loosening fault monitoring system has a good monitoring ability and can be used for vibration measurement. According to these monitoring results, the conclusions can be obtained as follows: first, the frequency domain data amplitude changes before and after loosening can be used to judge whether the bolt is loose, so as to achieve the monitoring purpose; Second, the strength of the vibration signal is large, the vibration signal change caused by the loosening of the bolt is submerged, and the installation of excitation at the sensor should be avoided to ensure that the monitoring is not disturbed by external factors. The research provides a new technical way for real-time monitoring of loose bolt fault of transmission tower, which has practical value and popularization prospect
Artificial intelligence-based stock market price prediction, a review
Full text linkThe stock market, a cornerstone of the global financial system, is characterized by its dynamic and volatile nature, which makes accurate price-trend prediction challenging. However, traditional statistical models often fail to capture this complexity. Recent advancements in Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), have transformed stock market forecasting by using diverse datasets and algorithms. This review examines recent studies on AI methodologies for stock market price trend prediction models by analyzing architectures, datasets, performance metrics, and limitations, with a focus on hybrid models, sentiment analysis, and dataset diversity. Hybrid approaches, including the Multi-Model Generative Adversarial Network Hybrid Prediction Algorithm (MMGAN-HPA), K-means long short-term memory (LSTM), and LSTM autoregressive output (LSTM-ARO), improve predictive accuracy by combining statistical methods with deep learning. Sentiment analysis models such as Stock Senti WordNet (SSWN) and Hybrid Quantum Neural Network (HQNN) integrate social media sentiment to capture market dynamics. Real-time frameworks that use stream processing show promise for high-frequency trading applications. This review addresses key challenges including data noise, nonstationarity, overfitting risks, and black-box model interpretability. Solutions include GAN-based synthetic data generation, transformer-based architectures such as SpectralGPT, and optimization techniques for computational efficiency. This review provides a taxonomy of AI-based approaches, while identifying gaps for future research. These findings highlight the potential of AI in financial forecasting while emphasizing the need for interdisciplinary collaboration to address its limitations in data quality, methodology, interpretability, and ethics
The effect of the quantity and length of fibers on the mechanical properties of fiber-reinforced concrete based on polypropylene fibers
Full text linkIn these studies, the effect of polypropylene fibers on the mechanical properties of concrete was studied, and special attention was paid to determining their optimal amount and acceptable length. The fibers were added to the concrete composition in amounts of 0.1-0.5 % and lengths of 10, 20, 30, 40, 50 mm and tested. According to the results of the study, the highest results were recorded at a fiber content of 0.2-0.3 % and lengths of 20-30 mm, and the compressive strength of concrete increased by up to 15.9 % compared to ordinary concrete. When adding fibers in excess (≥ 0.4 %) or with a length of 50 mm, a decrease in strength was observed. The results obtained showed that it is possible to increase the quality and improve the strength of concrete by selecting the optimal parameters of polypropylene fibers
Current research status on the occupational hazards of hand-transmitted vibration: a case study in China
Full text linkThe hazards arising from long-term hand-transmitted vibration operations can cause significant damage to the human body. As China is a populous country, understanding the current situation of vibration exposure among workers in various related fields in China holds significant reference value. To this end, this paper analyzes data from core journal literature in China from the 1980s to the present based on the keywords hand-arm vibration disease, hand-transmitted vibration, and occupational exposure. This paper provides an overview of the current status of hand-transmitted vibration hazards, including the distribution characteristics, hazards, and diagnostic methods of hand-arm vibration disease, as well as the deficiencies in these diagnostic methods. It also integrates data on the vibration intensity, frequency, and prevalence of vibration tools to analyze the relationship between the prevalence of hand-arm vibration disease and the vibration intensity and frequency of vibration tools. The results indicate that the vibration tools causing occupational hand-arm vibration disease are primarily found in the mining and manufacturing industries, with rock drilling jobs and positions being dominant in the mining industry and grinding jobs and positions being dominant in the manufacturing industry. The A(4) values of grinding tools, jobs, or positions are significantly higher than China’s limit value of 5 m/s2 for hand-transmitted vibration. The A(4) distribution of rock drilling tools is more concentrated, while the A(4) distribution of grinding tools is broader. The current diagnostic methods have poor specificity and sensitivity. There is insufficient awareness of the hazards of hand-transmitted vibration. There is no significant correlation between A(4) and the prevalence of vibration white finger (P>0.05), and A(4) alone is insufficient to reflect the extent of harm caused by hand-transmitted vibration operations to the human body. Both low-frequency and high-frequency vibrations may be harmful to the human body, and there may be a positive correlation between the fundamental frequency of vibration tools and the prevalence of disease (r>0,P<0.05)
LSGAN-Transformer life prediction method for rolling bearings under few samples
Full text linkAiming at the problem that it is difficult to obtain a large amount of data for bearings with complex working conditions, which leads to the inability to accurately predict their life, a rolling bearing life prediction method based on few samples, LSGAN-Transformer, is proposed. A dropout layer is added to the LSGAN generator to avoid the overfitting phenomenon that often occurs during few-sample training. The normalization of each layer in the traditional Transformer model is moved forward to the input of the decoder and encoder submodules before the residual network, forming a direct gradient path from input to output, avoiding the problem of excessive expected gradient near the output layer that often occurs in the traditional Transformer network. Verification on the PHM2012 dataset and the XJTY-SY dataset shows that the MAE and RMSE of the proposed method are greatly improved; compared with other common prediction models, the MAE and RMSE of the proposed method are improved by 30.61 % and 35.93 % respectively
Construction of a mathematical model of the motion of a vibrating separator with pneumatic suspension
Full text linkThe mathematical model of the movement of an arbitrary point of the container of a vibrating separator with a pneumatic suspension is built in the article. For modeling, a vibrating separator with two independently driven unbalances and a pneumatic suspension was chosen, which has a number of advantages over other separators, is characterized by simplicity of construction and maintenance, and low sensitivity to the properties of the medium being separated. The developed unified parameterized model of a vibrating separator can be used by changing its parameters or zeroing them for a wide range of designs of vibrating separators. The use of data from ready-made unified mathematical models allows you to reduce the duration of research and design of vibrating separators, and reduce material costs in general