Material Science, Engineering and Applications
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Experimental diagnostics of the condition and behavior of an excavation machine: a review of the most important methods
Full text linkThe paper presents an integral procedure for conducting experimental measurements on excavation machines. Excavators have a complex structure with pronounced dynamic behavior. The identification of exploitation behavior is observed through experimental measurement of stress and acceleration, drive load, and vibrations. Electro-resistive measuring tapes were used to observe the steel structure, devices for measuring current, i.e. engaged power on the drives, as well as devices for measuring vibrations at characteristic points of the drive. The results obtained realistically reflect the condition and behavior of the structure and drive equipment. The goal is to introduce systematic research to monitor the condition and behavior of the equipment on the excavator. This approach forms the backbone of predictive observation, influencing the proper management of the excavator. Experimental measurements are performed to prove the correctness of the numerical model and to diagnose the condition and behavior of the structure and power units. By monitoring the condition and behavior of the equipment, we can optimally influence the process of maintenance of the equipment as well as the lifespan of the mining machine. This work includes the most important experimental measurements to carry out reconstructions, revitalizations, and modernizations on mining machines
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
Active fuzzy control of a suspension vehicle on wet and dry roads
Full text linkThis paper presents a co-simulation of MATLAB and CarSim to control and model a vehicle suspension system under different road surface conditions, either wet or dry, using an active fuzzy controller in MATLAB. CarSim is a professional vehicle simulation software capable of modeling nonlinear car dynamics with various uncertainties. These uncertainties are addressed by the fuzzy set approach due to its qualitative and robust control capabilities, effectively handling noise, disturbances (such as road conditions), and unknown parameters in CarSim’s vehicle model. The design of an active steering controller and rotational torque system using a fuzzy controller is crucial for enhancing road safety, especially given the increasing number of vehicle crashes. The research methodology varies based on the study's purpose, nature, and implementation capabilities. Accordingly, this research focuses on designing an integrated controller for an active four-wheel-drive system and direct rotary torque control using a fuzzy control method in the MATLAB Simulink environment. This study is analytical and functional, utilizing CarSim for simulation. A fuzzy logic-based integrated control system was designed for steady-state control to improve vehicle stability and steering. The controller adjusts the steering angle and torque to regulate the vehicle’s angular velocity and slip angle under various conditions. As tire performance changes during different maneuvers, the controller dynamically adapts its output to maintain optimal operation within the effective performance range. The significance of using fuzzy logic lies in its ability to handle non-linearity without requiring approximation, ensuring high accuracy. Additionally, it delivers excellent results in enhancing vehicle stability. The findings indicate that the controller significantly improves the vehicle’s dynamic behavior across different driving maneuvers compared to an uncontrolled vehicle
High-speed roller/rail dynamics and thermodynamics considering surface roughness and revolution
Full text linkConsidering rotation and not considering rotation, a calculation was conducted on the friction and wear between the sliding pair in a high-speed rotating machine using a plane of Ra6.3 and Ra3.2, indicating that Ra3.2 has advantages. In higher firing rate Gatling guns, the guide rail should be processed more finely and have a smaller roughness. The results demonstrate that the stress increases a lot when the bolt is surface rough, which is 11.5 % higher than the flat condition. The temperature of Ra6.3 is about 100° higher than the flat condition. It plays an important role in improving the service life of friction surfaces
Study on the effect of suspension system friction of heavy-haul freight vehicles on the operation performance
Full text linkDuring the operational life of heavy-haul freight vehicles, the long-term wear between components can affect the suspension parameters. Suspension system wear has a significant effect on the dynamic performance and wheel wear. Experimental tests are performed to measure the changes in suspension system parameters after wear. A dynamic model and wheel wear model of the heavy-haul freight vehicles were established to analyze their dynamics and wheel wear performance. The results showed that with the wear of the suspension system, the stiffness parameters further increase. The dynamic performance of the vehicle system deteriorates after suspension system wear, with a decrease in the critical speed and an increase in safety and ride indexes. The analysis also reveals that the wheel wear increases as the stiffness parameters increase after the suspension system wear. This paper provides a basis for maintaining heavy-haul freight vehicle suspension systems
Permeability test of geotextile-soil system under different sand filling heights
Full text linkGeotube dams are constructed by stacking geotubes, which are non-homogeneous structures composed of geotextiles and filled sand. Therefore, studying the permeability characteristics of the geotextile-soil system is of great significance for seepage analysis in geotube dams. While the permeability characteristics of geotextiles and filled sand have been extensively studied individually, there has been relatively little research on the permeability characteristics of the geotextile-soil system formed by the combination of geotextiles and soil. In this study, a self-designed permeameter was used to investigate the permeability characteristics of the geotextile-soil system under different sand filling heights. The test results indicate that the permeability coefficient of the geotextile-soil system decreases continuously with the increase in permeation time and eventually stabilizes. The permeability coefficient of the geotextile-soil system increases with the sand-filling height and finally approaches but remains slightly smaller than that of pure sand with the same gradation. The influence of geotextiles on the permeability of the geotextile-soil system is significant within the range of 0 to 5 cm. Additionally, the water permeability of geotextiles affects the permeability performance of the geotextile-soil system. Specifically, a larger porosity corresponds to higher water permeability, and a greater permeability coefficient of the geotextile leads to a higher permeability coefficient of the geotextile-soil system
Feature data analysis of dance movements by motion capture
Full text linkMotion capture technology has been applied in more and more fields, but the research in the field of dance is relatively rare. In order to combine motion capture technology with dance research, better understand the characteristics of dance movements, and provide support for their digital analysis, this paper mainly studied the application of a motion capture technology called Kinect in the analysis of dance movement feature data. The skeleton data of different dance movements was first collected based on Kinect v2, and then the collected data was analyzed using a spatio-temporal graph convolutional network (ST-GCN). On the basis of the original ST-GCN, the multi-branch structure was adopted to realize co-occurrence feature learning, and the bone length feature and direction feature were introduced to further enrich the feature data. Experiments were carried out on the NTU RGB+D and dance datasets. It was found that the improved ST-GCN had better performance than other current motion classification approaches on the NTU RGB+D. The top-1 accuracy for cross-subject (CS) and cross-view (CV) was 92.4 % and 96.7 %, respectively, and the average accuracy of different dance movements for the dance dataset was 96.035. The findings confirm the effectiveness of the proposed approach in the analysis of dance movement feature data, and it can be applied in the actual research of dance movements
Complex fault diagnosis in wind turbine bearings: a hybrid approach combining the improved feature mode decomposition and convolutional neural networks
Full text linkThe complex noise interference and diverse fault-induced signals in vibration data from wind turbine equipment pose significant challenges for bearing fault diagnosis, including cumbersome methodologies, prolonged processing times, and compromised accuracy. To address these limitations, this study proposes a novel composite fault diagnosis framework that integrates Feature Mode Decomposition (FMD), Fast Spectral Kurtosis (FSK), and Convolutional Neural Network (CNN). While conventional Empirical Mode Decomposition (EMD) exhibits limited noise robustness and struggles to extract subtle fault signatures in composite failure scenarios, our approach employs FMD to decompose fault-related intrinsic mode functions (IMFs)and further filters the IMF components using fast spectral cliffs with enhanced feature separability. Subsequently, the Short-Time Fourier Transform (STFT) is applied to derive time-frequency representations, followed by Fast Spectral Kurtosis analysis to identify optimal demodulation bands for non-stationary signals. The energy spectrum of denoised signals is converted into grayscale images, serving as input to a tailored CNN architecture for hierarchical feature learning. Experimental validation demonstrates that this hybrid methodology achieves a fault recognition accuracy of 98 % under compound fault conditions, outperforming conventional EMD-based approaches in terms of noise immunity and diagnostic precision. Comparative analysis reveals an 8 % improvement in detection reliability over standalone deep learning models, particularly in low signal-to-noise ratio (SNR) environments. The proposed framework offers a robust solution for multi-fault identification in industrial Bearing machinery, demonstrating superior generalization capability across varying operational conditions
Fault diagnosis method for wind turbine rolling bearings based on adaptive deep learning
Full text linkIn response to the problem of difficulty in extracting fault features of rolling bearings in wind turbine transmission systems under complex working conditions, which limits the accuracy of fault diagnosis. This article proposes an Adaptive Deep Learning based Rolling Bearing Fault Diagnosis Method (ADLM). Introducing dynamic convolution into Convolutional Neural Networks (CNNs) can adaptively capture data features; At the same time, the fishing optimization algorithm (CFOA) was used to optimize the hyperparameters of the bidirectional long short-term memory network (BiLSTM), and the CFOA-BiLSTM network was constructed to fully leverage its advantages in time series analysis. The specific implementation steps are as follows: first, preprocess the collected vibration signals and divide the processed dataset into a training set and a testing set; Then, parallel adaptive convolutional neural networks (ACNN) are used to process the training set and extract spatial domain local features from the vibration signal; Then, the features extracted from the two branches are weighted and fused through a dynamic weight adjustment mechanism, and the fused features are input into the CFOA-BiLSTM network to further capture the time-dependent features of the signal; Finally, the extracted features are input into the classifier to complete model training, and the model performance is evaluated using a test set. Experimental verification shows that on the dataset of Southeast University, the diagnostic accuracy of the ADLM model reached 98.52 %, demonstrating good reliability, robustness, and superiority in the diagnosis of rolling bearing faults
A new self-adaptive anti-galloping device in suppressing conductor galloping in transmission lines
Full text linkConductor galloping is a serious threat to transmission line integrity, inducing excessive conductor tension that may lead to catastrophic failures including conductor breakage and tower collapse. This study proposes a novel self-adaptive anti-galloping device (SAGD) to mitigate galloping amplitudes and reduce associated risks. In this paper a novel self-adaptive anti-galloping device (SAGD) to mitigate galloping amplitudes and reduce associated risks was proposed. The structural design scheme of the device is provided, and its operation sequence was verified through static loading experiments. Conductor free-falling experiments validated the SAGD's vibration control performance, with test results demonstrating its practical applicability for transmission line protection. A finite element model for the conductor-SAGD system was developed, enabling numerical simulation of galloping displacement time history and analysis of endpoint support reaction dynamics. The device's galloping suppression effectiveness is systematically evaluated under varying stroke lengths and threshold conditions