Material Science, Engineering and Applications
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Identification and analysis of pavement structure features based on vibration behavior parameters
Get PDFTo clarify the correlation between the service performance of asphalt pavement structures and their vibration behavior parameters, this study focuses on asphalt pavement structures as the primary research subject. A quarter-vehicle two-degree-of-freedom model of a standard vehicle was selected as the simplified vehicle dynamics model, while a semi-rigid asphalt pavement was adopted as the simplified pavement model. Based on the elastic layered system theory, a three-dimensional finite element model of the asphalt pavement was constructed by using the software of Abaqus. The effects of modulus variations in asphalt pavement structural layers on modal frequencies were analyzed. The impacts of coupled working conditions, such as structural layer cracking positions and interlayer failure, on the modal frequencies of asphalt pavement were investigated. Additionally, the attenuation process of dynamic responses in asphalt pavement structures under transient impact loads was examined. Building on this, the dynamic response behaviors of asphalt pavement structures under working conditions including structural layer cracking and interlayer failure were studied. The results demonstrate that as the vertical depth of the asphalt pavement structure increases, the modulus attenuation of structural layers significantly affects the overall modal frequencies and vibrational effects. When internal cracking and interlayer failure coexist in the asphalt pavement structure, the vibration acceleration characteristics under load align more closely with those of interlayer failure, while the vibration displacement exhibits greater magnitudes
Improved APF-based path planning for aircraft towbarless towing vehicle system
Get PDFTo enhance the maneuvering efficiency and safety of the aircraft towbarless towing vehicle (TTV) system, this study presents an optimized path planning method based on an improved artificial potential field (APF) algorithm. First, comprehensive kinematic and dynamic models are established, incorporating both lateral and yaw motions of the TTV system. Second, to mitigate obstacle interference challenges in complex airport environments, the proposed method introduces an innovative relative-distance safety factor and implements a dual-repulsive-force cooperative planning strategy, effectively overcoming the traditional APF algorithm’s limitations regarding goal unreachability and local minima. Furthermore, the integration of Bézier curves ensures curvature continuity in the planned path, thereby maintaining compliance with kinematic constraints. Finally, a constrained-motion TTV simulation model is developed to validate the algorithm’s performance. Simulation results demonstrate that, in static obstacle scenarios, the proposed method successfully enables autonomous path planning, generating smooth and collision-free trajectories. This approach offers a robust solution for ensuring stable and reliable operation of the TTV system in real-world airport environments
Research on converter transformer state early warning system based on confidence ratio-EEMD and multi-cascade network
Get PDFAiming at the problems of poor prediction effect of non-stationary parameters and single warning rule of UHV converter transformer, this study proposes an intelligent warning method based on decomposition-multi-level cascade network and fuzzy set. Firstly, the integrated empirical modal decomposition technique is used to decompose the target parameter sequence into multiple sub-sequences, and the effective components are screened by the DPR-KLdiv confidence ratio, which is dynamically grouped and reconstructed to form a multilevel feature input; and the multilevel cascade network is constructed by combining multi-device parameters to make the time series prediction. The fuzzy function is further introduced to establish the parameter state mapping rules to expand the alarm triggering conditions. The experiments are validated by actual equipment data, and the local discharge signals of different defects are detected by ultra-high frequency method to enhance the generalization ability of the parameters. The results show that the average RMSE and MAE of this method are 23.21 and 18.47 respectively under the hours step prediction, and the accuracy of the warning is over 90 %, which effectively improves the accuracy of non-smooth parameter prediction and the flexibility of the warning decision
Multi source heterogeneous data diagnosis method of rotating machinery based on parameter collaborative optimization of multi-scale convolutional autoencoder
Get PDFIn order to fully utilize the features of multi-source heterogeneous data and effectively improve the accuracy and efficiency of fault diagnosis of rotating machinery, a multi-source heterogeneous data diagnosis method based on parameter collaborative optimization multi-scale convolutional autoencoder (MSCAE) is proposed. Firstly, multi-scale information learning is integrated into the convolutional autoencoder (CAE) to consider the temporal and spatial feature information of the diagnostic object simultaneously. To improve the training and diagnostic efficiency of MSCAE, a quantum particle swarm optimization (QPSO) module is used to perform hyperparameter optimization on it using chaos initialization and dynamic weight strategy (DWS). Besides, the sparse attention mechanism is introduced into the MSCAE model to improve the recognition rate of key fault features hidden in the original heterogeneous signals. Finally, the confusion matrix and visualization techniques are used to achieve fault classification. The experimental results demonstrate that after 100 experiments, the proposed method has an average diagnostic accuracy of 98.5 % and strong robustness to noise, providing a new method for rotating machinery fault diagnosis based on multi-source heterogeneous data
Application of GSABO-VMD-KELM in rolling bearing fault diagnosis
Get PDFTo address the difficulties in extracting fault features of rolling bearings and the low diagnostic accuracy, a fault diagnosis method for rolling bearings is proposed. This method integrates the Golden Sine Algorithm (GSA) with the Subtraction-Average-Based Optimizer (SABO) to form a Golden Sine Improved SABO Optimization Algorithm (GSABO). The GSABO algorithm is used for parameter optimization of Variational Mode Decomposition (VMD) and Kernel Extreme Learning Machine (KELM) in the fault diagnosis process. Firstly, the chaotic mapping strategy is used to optimize the population initialization of the Subtractive Clustering-Based Adaptive Optimization (SCAO) algorithm, enhancing population diversity. Secondly, the Golden Sine Algorithm (GSA) is integrated to improve the displacement algorithm, enhancing global search capability and effectively avoiding getting trapped in local optima. Then, the GSABO-VMD (Golden Sine Algorithm-Based Optimized Variational Mode Decomposition) is employed to decompose the rolling bearing fault signals, and the envelope entropy minimum criterion is used to select the effective modal components. Finally, time-frequency domain indicators of the selected modal components are computed to form a feature matrix, which is then input into GSABO-KELM (Golden Sine Algorithm-Based Optimized Kernel Extreme Learning Machine) for fault classification and recognition. Experimental analysis shows that compared to the unmodified SABO algorithm, GSABO has significant advantages in terms of escaping local optima, convergence speed, and accuracy. When compared with other traditional algorithms, GSABO-VMD-KELM achieves recognition accuracies of 99.3333 % and 99.0476 % on bearing data from Case Western Reserve University (CWRU) and Xi'an Jiao tong University (XJTU), respectively. This demonstrates the accuracy and superiority of the algorithm and provides valuable insights for engineering applications in rolling bearing fault diagnosis
Random vibration analysis and mechanical performance research of large-span spatial structures using new building materials
Get PDFIn order to analyze the performance of large-span spatial structures made of new building materials, improve the seismic resistance of large-span spatial structures made of new building materials, analyze the random vibration of large-span spatial structures made of new building materials, and determine the mechanical properties of large-span spatial structures made of new building materials. The paper takes carbon fiber reinforced polymer (CFRP) as an example, and prepares CFRP large-span structural specimens through surface coating treatment of carbon fiber and composite material preparation process; Enhancement effect of interfacial bonding strength of CFRP large-span spatial structures through bidirectional shear experiments; Design large-span spatial structures of carbon fiber composite buildings and establish multi-scale finite element models of vibration reduction systems; Analyze the random vibration of large-span spatial structures, improve the Kanai Tajimi model through the random vibration power spectral density function, calculate the structural response power spectrum, analyze the response of CFRP large-span spatial structures through the H-V coherence function model, and verify the mechanical properties of CFRP material large-span spatial structure specimens through experiments. The test results show that after the tensile test, the CFRP specimen connecting plate did not fail, indicating that the CFRP specimen has a significant impact on its connection strength in this situation. However, the compression and shear failure of the CFRP large-span spatial structure specimen will occur in local areas due to the compressive action of the specimen
Research on fault diagnosis of rolling bearings based on multi-method fusion
Get PDFTo address the limitation that Variational Mode Decomposition (VMD) relies on empirical settings for the mode decomposition number K and penalty factor α, this paper proposed the RIME-VMD-KNN method for bearing fault diagnosis. Specifically, the RIME algorithm was used to intelligently optimize K and α of VMD, breaking the reliance on experience; Pearson Correlation Coefficient (PCC) was adopted to screen Intrinsic Mode Functions (IMFs) with high fault correlation for signal reconstruction, preserving key features; and the sample entropy of the reconstructed signal was input into KNN for fault identification. Experiments show that the optimization performance of RIME is superior to that of GA, GWO and AOA; the generalization ability is verified by supplementary tests on the XJTU-SY dataset; KNN is simpler and more efficient than SVM, proving the rationality of its selection; the confusion matrix and multiple random cross-validation confirm stability; and computing time and resource data are provided to verify the feasibility of embedded deployment. This method improves the reliability and real-time performance of diagnosis and has engineering value
Analysis of modal and vibration response characteristics of high-pressure storage tanks
Get PDFAnalysis of dynamic characteristic was conducted focusing on the transportation of high-pressure storage tanks, covering two scenarios: independent transportation and mixed transportation. For independent transportation, analysis of free modal was carried out to obtain the first four orders of modal shapes. Additionally, the influence of two constraint methods on the modal characteristics and stress distribution was studied, including fixed at both ends and fixed at the cylinder body. Results show that when the tank was fixed at the cylinder body, it had a higher natural frequency and a lower stress level, making it safer. For mixed transportation, a finite element model was built for 6 high-pressure storage tanks, and analysis of random vibration was performed. The results showed that stress was mainly concentrated on the crossbeams and connection nodes, while the stress on the main body of the storage tanks was relatively low. The overall structure exhibited excellent fatigue performance and met the mechanical and safety requirements under random vibration conditions
Scientific and practical substantiation of transient processes in asynchronous electric motors of mainline electric locomotives
Get PDFThe research work focuses on scientifically substantiating the operating conditions of small and medium-power auxiliary asynchronous electric motors used in mainline electric locomotives under JSC “Uzbekistan Railways”. The aim is to provide a scientific basis for the operational efficiency of auxiliary asynchronous electric motors and, based on the research findings, to conduct a practical investigation of their service life. This, in turn, will enable timely maintenance of auxiliary asynchronous electric motors in locomotives. Additionally, it will contribute to improving the performance indicators of auxiliary asynchronous electric motors
