Jaw Functional Orthopedics and Cranoficial Growth
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PSO-PPO-based reinforcement learning control strategy for active suspension systems under multiple operating conditions
Get PDFTo address the poor generalization capability and extended training duration of reinforcement learning (RL)-based active suspension control systems, this study proposes a PSO-PPO algorithm for multiple operating condition suspension control. The methodology initiates with establishing a 4-DOF suspension dynamic model under three characteristic driving conditions: constant-speed operation, vehicle launch, and emergency braking, which is subsequently converted into state-space representation. The novel PSO-PPO framework synergizes particle swarm optimization with proximal policy optimization to train condition-specific agents. Based on the trained optimal agents, the entropy weight method is applied to adjust the reward function weight coefficients to develop a generalized multi-condition controller. Finally, the control effectiveness of the PSO-PPO algorithm is validated through constant-speed, launch, emergency braking, and multi-condition concatenated scenarios. Simulation results demonstrate that the PSO-PPO algorithm achieves shorter training times while maintaining balanced performance in ride comfort, handling stability, and safety across all conditions
Dynamic detection and evaluation of wheel flats in heavy-haul railway wheelsets using wayside monitoring systems
Get PDFIn recent years, heavy-haul railways have become a critical direction for freight transport in China, with wheel flats in wheelsets posing significant threats to operational safety and infrastructure integrity. Traditional detection methods (e.g., manual inspection, TPDS) suffer from low efficiency or limited accuracy in characterizing flat features. To address this, this study develops a rigid-flexible coupling dynamic model for C80 wagons with K6 bogies, uniquely integrated with field data from the Truck Operation Detection System (TODS) to bridge simulation and engineering application gaps. Focusing on wheel-rail force responses under wheel flat conditions, we establish a quantitative mapping relationship between flat length, vehicle speed, and impact force through polynomial fitting of simulation data (10-80 km/h for empty/loaded vehicles). To validate feasibility, a 56-channel wayside monitoring system (TODS) is installed on a heavy-haul railway, calibrated via hydraulic loading to ensure measurement accuracy. Field tests (80,541 vehicles monitored) confirm that TODS can infer flat length from detected impact forces, with results consistent with TPDS alarms but offering finer characterization of flat dimensions. This work provides a practical solution for real-time wheel flat detection, enhancing maintenance efficiency and safety in heavy-haul operations
Fault diagnosis method for wind turbine rolling bearings based on adaptive deep learning
Get PDFIn 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
Mathematical modeling of the rotating drum granular fill flow oscillatory stability
Get PDFDrum-type machines have become widely used in many industries for processing various granular materials. An innovative direction for significantly increasing the energy efficiency of such equipment is the use of self-oscillating working processes. Self-excitation of auto-oscillations allows you to bring into pulsating flow and activate the passive part of the intra-chamber filling and significantly enhance the interaction of granular particles with each other and with the surrounding environment. The purpose of the study is to build a mathematical model of the conditions and factors of oscillatory instability of the flow of polydisperse granular filling in the chamber of a rotating drum. The research methodology includes analytical modeling of wave processes and experimental modeling of manifestations of instability of the filling flow. The inertial mode of flow of the active part of the filling in a shear flow state is analyzed, the behavior of which is described using averaged values. Based on the results obtained, an increase in instability with an increase in the dilatancy of the medium during deformation is established and the destabilizing effect of the damping action of the fine fraction on the interaction of particles of the coarse fraction is revealed. The main scientific novelty of this study is the identification of the regularities of the unsteady motion of the oscillatory system of a filled drum. The study confirms the possibility of generating, under certain conditions, self-excitation of auto-oscillations of the intra-chamber filling, which is a decisive factor in the predicted intensification of the technological process. The results obtained are valuable for researchers and engineers involved in the study and design of innovative energy-efficient working processes of drum machines
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
Structure design and sensitivity analysis of flexible ultrasonic transducer array
Get PDFTo investigate the influence of element parameters on the performance and acoustic field of flexible ultrasonic transducer arrays, this study employs finite element multiphysics simulation software to analyze various parameters of flexible ultrasonic transducers within a multiphysics coupled field. The analysis begins with simulating the width and thickness of piezoelectric materials in a single-element ultrasonic transducer structure. Simulation results indicate that the electromechanical coupling coefficient of the ultrasonic transducer exhibits a quasi-sinusoidal relationship with width. When the piezoelectric material width is 1.8 mm, the electromechanical coupling coefficient reaches its maximum at a thickness of 0.4 mm. Subsequently, simulations were conducted on various parameters of the flexible ultrasonic transducer array. Key investigations included the effects of piezoelectric unit count, inter-unit spacing, and frequency on the ultrasonic focusing performance of linear phased array transducers. Findings indicate that the focusing capability of flexible ultrasonic transducer arrays improves with reduced spacing and increased unit count. However, due to varying practical application requirements and manufacturing precision constraints, array parameters should be selected by comprehensively considering real-world factors. Overall, this study employs multiphysics coupling simulation to visually demonstrate how array element parameters influence the performance of flexible ultrasonic transducers. It provides valuable reference for advancing flexible ultrasonic technology from laboratory research toward commercial application
Improved APF-based path planning for aircraft towbarless towing vehicle system
No full textTo 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
No full textAiming 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
Prediction of wharf subsidence deformation degree based on deep learning technology
No full textThis paper presents an algorithm that combines a convolutional neural network (CNN) with a gated recurrent unit (GRU) to predict the wharf subsidence deformation. First, the digital elevation model (DEM) image features of the wharf area were extracted using the CNN, and then the patterns of change in wharf settlement were captured using the GRU. Moreover, the wharf in the Longtan Port area of Nanjing Port, located in Jiangsu Province, was analyzed. When the CNN comprised three convolutional layers and the activation function was set to sigmoid, the prediction performance of the proposed algorithm was the best. In both short-term and long-term scenarios, the CNN+GRU algorithm had better prediction performance than long short-term memory and GRU models
Vibration velocity control of compound wedge-shaped excavation blasting in tunnels under complex environments
No full textThis paper explores the optimization of cutout schemes in tunnel excavation and blasting by introducing an improved segmented wedge-shaped blasting method, validated through both numerical simulations and field tests. The numerical simulations use the ANSYS/LS-DYNA fluid-solid coupling algorithm to analyze the damage effects, effective stress distribution, and vibration characteristics of surrounding rock for both the Conventional wedge cut and the segmented wedge cut methods. The results show that segmented wedge cutting significantly enhances the utilization rate of blast holes, reduces the formation of large gravel fragments, and effectively mitigates surrounding rock vibration velocities. In comparison to the Conventional method, the optimized undercut scheme not only increases blasting efficiency but also greatly enhances the rock fragmentation in the undercut area, thereby ensuring tunnel construction safety. The field test results validate the accuracy of the numerical simulations and show that the enhanced scheme holds significant potential for practical application in real-world projects