ISSN 1009-0134
CN 11-4389/TP
主管：中国机械工业联合会
主办：北京机械工业自动化研究所有限公司
CN 11-4389/TP
主管：中国机械工业联合会
主办：北京机械工业自动化研究所有限公司
中国科技核心期刊
中国期刊方阵双效期刊
机械行业优秀期刊
中国期刊方阵双效期刊
机械行业优秀期刊
Volume 47 Issue 6
25 June 2025
25 June 2025
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
To address the problems of scattered knowledge systems in ship assembly and ineffective mining and utilization of massive process data， this paper proposes an automatic knowledge graph construction technology for the shipbuilding domain based on large language models. This method uses large language models to convert unstructured and semi-structured ship data into structured data to build a ship process corpus. It models ship ontology knowledge structure with the assistance of large language models， designs an instruction prompting framework for ship assembly domain， and achieves efficient entity-relationship extraction， to complete the automatic construction of knowledge graphs. Additionally， the method uses triple sets constructed by general large language model instruction prompts as fine-tuning training sets to further fine-tune specialized small language models， ensuring the security of specific private ship data while reducing computational resources. Experimental results show that this method outperforms traditional baseline models in key metrics such as accuracy， providing a new technical approach for knowledge management and intelligent upgrading in the shipbuilding domain.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
Tool wear prediction is of great significance for reducing costs， improving efficiency and ensuring machining quality. To address the challenges such as difficulties in extracting features related to tool wear information， low utilization rate of the extracted features， and low prediction precision and accuracy， under the circumstances of complex environmental noise and a low signal-to-noise ratio， a Multi-scale Sample Reconstruction （MSR） method for vibration signals was first proposed to mitigate the impact of noise on the prediction effect of subsequent models. Subsequently， an improved model was put forward， which was based on the integrated model of the Residual Network （ResNet） and the Bidirectional Long Short-Term Memory （BiLSTM） network. In this improved model， the Criss Cross Attention （CCA） mechanism was integrated into each residual layer， and a Stacked Bidirectional Long Short-Term Memory Network （SBILSTM） was adopted. By comparing this improved model with the ResNet-BiLSTM model as well as traditional deep learning models， the results demonstrated that this method significantly enhanced the prediction precision and accuracy of tool wear.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
In airport baggage handling system equipment failures， bearing faults account for the majority of the causes. Therefore， an effective bearing health management method is urgently needed. Traditional methods often lack efficient signal denoising and feature enhancement in vibration feature extraction. To address this， this paper proposes a bearing remaining useful life （RUL） prediction method that combines exponential smoothing and a Dual-Focus Prediction Network （DFPN）. First， the vibration data is denoised using exponential smoothing to improve signal quality， followed by the calculation of time-domain features for fusion. Then， a convolutional layer is used to compress the extracted features， and the DFPN dual-attention network is applied to extract spatial and channel-specific features separately. During the decoder phase， an attention mechanism is employed to weight the features， ultimately generating the predicted sequence for the bearing's remaining useful life. Experimental results show that the proposed method performs excellently on the FEMTO-ST and Xi'an Jiaotong University datasets， achieving improvements of 8.91% and 2.60% in Root Mean Square Error（RMSE）， and 37.17% and 12.63% in Mean Absolute Error （MAE）， respectively， compared to the second-best existing method.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
Aiming at the problems of low sample utilization rate， slow training convergence speed， and poor path planning performance in deep reinforcement learning， an improved path planning algorithm based on Dueling DQN is proposed， and directional rewards and filtering strategies are introduced. According to the angle between the line connecting the current state and the next moment state and the line connecting the current state and the target point， the reward function is redesigned to alleviate the problem of sparse rewards. During training， the actions resulting in collisions with obstacles are put into the blacklist， so that the action can be filtered in the next round of action selection， and the exploration speed of the algorithm is improved. The experimental results show that the improved algorithm can effectively improve the efficiency of path planning， and the exploration efficiency of the agent in complex environment is increased by about 95%， so that the agent can reach the target point with fewer exploration steps and less time.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
Aiming at the issues of poor four-wheel steering performance and reduced vehicle maneuverability caused by heavy loads， long wheelbases， narrow mining area roads， and complex road conditions， this paper proposes an active four-wheel steering control method based on an improved PSO-LQR （Particle Swarm Optimization-Linear Quadratic Regulator）. The method utilizes an improved PSO algorithm to determine the appropriate weight matrices， enabling dynamic adjustment of LQR algorithm according to vehicle state changes to get the optimized feedback coefficients. By improving the LQR algorithm based on a two-degree-of-freedom four-wheel steering model， the vehicle can track ideal yaw rate and sideslip angle in real time. The approach was subsequently validated using a seven-degree-of-freedom vehicle dynamics model for mining trucks. Four experimental scenarios were employed to verify the effectiveness of the proposed algorithm： In the sinusoidal steering input scenario， tracking performance comparisons were made among LQR， PSO-LQR， and SMC （Sliding Mode Control）； Under high steering angle rate change condition， the dynamic weight coefficient adjustment characteristics of the improved algorithm were demonstrated， verifying the real time computation of the PSD algorithm； Under steady-state circular motion scenario， the impacts of the LQR and PSO-LQR algorithms on the dynamic performance of the vehicle with the proposed active four-wheel steering were analysed and compared； The double-lane change test on gravel roads verified the robustness of the improved algorithm. Simulation results indicate that the proposed PSO-LQR-based active four-wheel steering achieves higher tracking accuracy. Moreover， the PSO algorithm exhibits computational simplicity， enables dynamic weight coefficient adaptation to various working conditions， and demonstrates strong robustness and real-time performance.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
This study aims to propose an improved motion model for tobacco particles in the drum dryer. It also explores a method to estimate the residence time and other indicators when the inherent parameter values and operating conditions are known. By considering multiple motion forms of tobacco in the dryer and estimating the real-time positions of tobacco particles， we can accurately estimate the residence time in the drum. This estimation is significant for controlling the moisture and temperature of tobacco. It provides valuable information and improvement directions for enhancing tobacco processing quality. Numerical simulation results indicate that， under the condition where inherent parameters cannot be adjusted， the residence time and contact heating time are more sensitive to the rotation speed of the dryer compared to the hot air velocity. Therefore， when optimizing the drying process， controlling the rotation speed of the dryer as an effective operating variable can achieve precise control of tobacco moisture content.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
Wireless Power Transfer （WPT） technology can effectively improve the convenience， efficiency， and safety of charging Unmanned Aerial Vehicle （UAV）， thus increasing the flight duration of UAV. However， it also faces critical problems such as the decrease in energy efficiency due to the difficulties in accurate positioning between coils. Therefore， this paper proposes a method for wireless charging localization of arrayed UAVs based on passive beacons. The article firstly constructs a mathematical model of the WPT positioning system； secondly， a method based on passive beacon coils to locate the receiving coils is proposed for the arrayed many-to-one WPT system， which realizes the position detection of the many-to-one WPT system； finally， the position detection analysis is carried out under three working conditions： the transverse offset， the longitudinal offset， and the 45° angular offset， relative to the receiving coils. The optimal transmitting coil with the best electromagnetic state is used to ensure that the energy transfer efficiency of the system is always in a highly efficient working condition.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
Due to the non-cooperative game characteristics between the random field of photovaltaic （PV） output and the inertial response of the grid， it is difficult to determine the distributed PV access capacity target based on the voltage of the distribution grid node after distributed PV power access， easily leading to voltage over-limit problem， resulting in large fluctuations in the per-unit value of the controlled node voltage. To address these challenges， a dynamic balancing control method for distributed PV power access capacity based on the Adaptive Particle Swarm Optimization （APSO） algorithm is proposed. The distributed PV power access distribution network node voltage model is constructed to analyse the voltage changes before and after the distributed PV power access to the distribution network， and update the node voltage of the distribution network after the distributed PV power access； the updated access voltage is used to construct a two-layer objective function of the distribution network， with the upper layer taking the minimum deviation of the node voltage of the grid as the multi-objective to determine the access cost of the distributed PV power supply， and the lower layer determining the access capacity objective of the distributed PV power supply to avoid the voltage from exceeding the limit， while setting constraints； using the APSO algorithm to solve the two-layer objective function under the constraints， find the optimal objective function results， determine the optimal dynamic balancing control results of the access capacity of distributed PV power， to ensure the safe and stable operation of the distribution network. The experimental results show that， under various extreme node load conditions in the distribution network， the algorithm can dynamically balance the capacity of distributed PV power supply to access the nodes of the distribution network， and after control， the per-unit values of each node voltage can be maintained between 0.96 and 1.02， the voltage fluctuation is smaller and more balanced， while the degree of imbalance can be controlled below 0.09， which meets the voltage operation standard and can ensure the operational safety.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
In order to adapt to the characteristics of complex electronic equipment， such as multi-variety， variable batch， multi-level blind matching and vertical interconnection， a six-degree of freedom heterogeneous assembly robot arm has been independently developed and applied to the assembly line of complex electronic equipment. Firstly， the structure composition， configuration advantages and problems in practical application of the heterogeneous six-axis manipulator are introduced. Secondly， the forward and inverse kinematics algorithm of the heterogeneous six-axis manipulator is established by using D-H parameter method， and the kinematics model of the heterogeneous six-axis manipulator is constructed. Then the calibration algorithm， trajectory planning algorithm， collision control algorithm and other methods of the heterogeneous six-axis manipulator are studied. Finally， the field calibration experiment and MATLAB simulation analysis are used to verify the motion planning method， which proves the rationality and practicability of the relevant methods.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
To ensure the safety and stability of the power transmission systems and achieve comprehensive inspection and maintenance of newly constructed power transmission towers， this paper proposes a centrally symmetric quadrupedal humanoid climbing robot designed for existing foot pegs used by maintenance workers to climb towers. Each limb is configured with 3-1-2 arrangement. At the end of each limb， a large-tolerance， semi-enclosed hook-type gripping tool is designed specifically for the foot pegs. This tool features high tolerance， eliminating the need for precise end-effector positioning and enabling rapid engagement with the foot pegs. Humanoid climbing gait planning method is developed， facilitating the robot's full-range climbing of the power transmission tower by quickly hooking and gripping the foot pegs using the hook-type tool. Targeting a 40-meter-high self-supporting transmission tower， the robot's full-range climbing dynamics model and simulations were completed. Simulation results demonstrate that the proposed robot configuration can achieve humanoid full-range climbing of the tower， with a climbing time from the base to the top of less than 30 minutes， matching the efficiency of maintenance personnel. This provides a feasible solution for robotic maintenance applications in power transmission towers.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
To improve the object grasping success rate and pose prediction accuracy of robots through visual information in unstructured scenarios， a grasp detection model combining convolution and self-attention， i.e.， the Unified transformer grasp network （UFGNet）， is proposed. UFGNet uses an encoder-decoder structure. In the encoding phase， a layered Transformer module is adopted. The multi-head relationship aggregator in this module integrates the advantages of convolution and self-attention， and effectively extracts rich multi-scale features and global information at the shallow layer and deep layer respectively. The residual structure in the decoding stage enables the model to learn more complex feature representations， and pays more attention to the grasping area through the Shuffle Attention module， thereby improves the grasping detection performance. To verify the performance of UFGNet， the Cornell data set and Jacquard data set were used to train and test UFGNet， and the PyBullet simulation platform was built for capturing experiments. The results show that UFGNet has achieved 98.4% and 94.9% accuracy on the Cornell dataset and Jacquard dataset respectively， demonstrating competitive performance. In the simulation experiment， the average grasping success rate is 95.8%， and the experimental results verify the accuracy and robustness of UFGNet in grasping objects.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
To address the challenge of locating leak sources in pressure vessels under complex operating conditions， this study proposes a leakage detection method based on a robust adaptive beamforming algorithm. The proposed algorithm enhances beamforming robustness through steering vector optimization combined with covariance matrix reconstruction. Specifically， it constructs a projection matrix of the signal subspace to perform projection correction on steering vectors， thereby reducing errors introduced by steering vector estimation and obtaining more accurate desired signal steering vectors. Concurrently， the generalized linear combination approach is employed to reconstruct the covariance matrix， achieving robust beamforming technology. Numerical simulation and experimental results demonstrate the effectiveness of the algorithm in leak source localization， showing that the robust beamforming approach accurately pinpoints gas leak sources， achieving an average detection success rate exceeding 93% with a maximum positioning error below 0.03 m. Compared with conventional leak detection methods， this approach exhibits superior localization resolution and accuracy under challenging noise conditions characterized by low sampling rates and low signal-to-noise ratios （SNR）， significantly enhancing the engineering practicality of pressure vessel leak detection systems.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
Aiming at the challenges such as insufficient feature extraction capacity， and low accuracy in the fault diagnosis for asynchronous motors， an asynchronous motor fault diagnosis model based on convolutional neural network （CNN）-Transformer-BiLSTM is proposed. By linking large-scale convolutional kernels with the BiLSTM module to extract the temporal features of fault vibration signals， the model uses a gate mechanism and bidirectional temporal learning mechanism to effectively learn the feature relationships between fault signals at multiple moments. By connecting small-sized convolutional kernels with the Transformer module， the model further increases its ability to extract temporal features. The model uses a multi-head attention mechanism to achieve parallel and efficient processing of feature sequences and outputs a diagnosis result through a Softmax classification. Under 10 dB noise interference， the proposed model was compared with the MSCNN-LSTM-Attention， MSCNN， BiLSTM， and 1DCNN models. The comparison test results show that this model can effectively extract fault features， and the fault diagnosis accuracy rates were increased by 4.3%， 9.7%， 17.2%， and 18.5% respectively， demonstrating that this model has a higher fault accuracy under noise interference.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
Bearing fault detection plays a crucial role in the health monitoring and maintenance of industrial equipment. However， traditional methods struggle to balance global modeling capability and computational efficiency when processing ultra-long time-series signals and complex operating conditions， leading to reduced detection accuracy and limited inference speed. To address this issue， this paper proposes a fault detection method based on Noise-Adaptive Multivariate Variational Mode Decomposition （NA-MVMD） and Adaptive Local Convolution-Attention Transformer （ALCAT） to improve accuracy， robustness， and computational efficiency of fault detection. Specifically， NA-MVMD introduces a noise-adaptive mechanism to optimize multivariate signal decoupling， enhancing the stability and reliability of feature extraction. Meanwhile， ALCAT integrates Signal-Adaptive Segmentation （SAS）， Convolutional Neural Networks （CNN）， Attention Mechanisms， and Transformer structures， enabling efficient local and global feature learning， thereby improving fault recognition accuracy and inference speed. To validate the effectiveness of the proposed method， we compare NA-MVMD-ALCAT with multiple advanced models. Experimental results demonstrate that the proposed approach outperforms all comparison methods across various performance metrics， achieving an F1 score of 0.9863， which highlights its superior fault detection capability. Moreover， when the window size is set to 1024， the model achieves an optimal balance between detection accuracy and computational efficiency， effectively handling ultra-long time-series data. Overall， NA-MVMD-ALCAT is well-suited for online bearing fault monitoring and intelligent maintenance systems， providing an efficient and accurate solution for industrial equipment health monitoring.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
Aiming at the problems of insufficient feature extraction of the vibration signals of the rocker gear of coal mining machine and slow convergence of the traditional deep learning model， a fault diagnosis method combining dilated convolution kernel and residual connection is proposed. The dilated convolution kernel can effectively capture the local feature information in the vibration signals while expanding the receptive field， meanwhile the residual connection can accelerate the model training and improve the model performance by alleviating the gradient vanishing problem. In order to verify the effectiveness of the method， experiments are conducted on the collected gear fault data in the DDS transmission system fault diagnosis experimental bench. The experimental results show that the model in this paper outperforms CNN， ResNet-1D， MobileNet-1D， Inception-1D and Dilated-CNN methods in terms of accuracy， convergence speed and stability. The diagnostic accuracy is more than 95% in the five planetary gear states of normal， cracked， fewer teeth， worn and broken teeth. Under the condition that the signal-to-noise ratio is not less than 10dB， the method can realize gear fault diagnosis accurately and efficiently.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
This paper focuses on optimizing the configuration of resources， such as machine types and quantities， in assembly production lines to improve production efficiency， reduce costs， and ensure smooth operations. First， data from the production line is collected， and the bottleneck processes and resource utilization are analyzed. Next， a mathematical model for the optimization problem， with constraints on production efficiency and cycle time， and the optimization objective of minimizing configuration costs， is established based on Petri nets. The Arctic Puffin Optimization （APO） algorithm is then used to find the optimal configuration solution. Finally， the optimized solution is applied to a simulation of the automotive engine assembly line for verification. This optimization method can effectively enhance the production line efficiency， reduce operational costs， and adapt to market demand changes， offering significant practical value in the field of intelligent manufacturing.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
An improved starfish search algorithm was proposed to optimize workshop facility layouts， so to address the existing problem of high handling costs and time. Latin hypercube sampling was used to diversify the initial population. Levy flight and Brownian motion strategies are introduced to enhance the diversity of solutions and local exploration capabilities， achieving a balance between global and local search during the iteration process. Algorithmic complexity analysis indicates no increase in computational burden. Performance comparisons against 5 algorithms （SFOA， GA， PSO， GWO， and WOA） with 12 benchmark functions from CEC2017 demonstrates that the improved starfish search algorithm yields superior mean and standard deviation results over the other 5 algorithms in most cases. It reduces transportation costs by 44.47%， transportation time by 46.33%， and the objective function value by 44.81%. This method significantly enhances layout optimization efficiency and solution accuracy， making it practical and feasible for real-world applications.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
Spiral baffle is an important element in shell and tube heat exchangers. Effectively obtaining the spatial position information of the curved spiral baffle surface is the key to solving the tube hole machining of spiral baffle. Aiming at the problems of weak texture， multiple occlusions， reflective surface， and difficulty in meeting industrial requirements on accuracy and time for 3D reconstruction， this paper proposes a matching algorithm that combines feature detectors with dense vision and a variable field-of-view attention module， to obtain more feature points in weekly textured and reflective areas； The cascaded CasMVSNet architecture for dense point cloud reconstruction for spiral baffles is used to reduce surface voids and shorten reconstruction time. The experimental results on the self collected dataset show that the proposed 3D reconstruction technique yields a perfect spiral baffle surface with almost no voids or noise in weak texture areas. Compared to other algorithms， the reconstruction accuracy has improved by 3.96%， the cumulative error curve area of pose estimation has increased by 6.48%， and the reconstruction time has been reduced to within 180 seconds. This proves the reliability and effectiveness of the reconstruction technique.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
Internal fault diagnosis in large transformers is challenging. Robotic fish， due to their small size and agility， are gaining increasing attention for transformer inspections. However， their limited battery capacity necessitates frequent recharging. To address this， wireless power transmission （WPT） technology has emerged as a key research area for continuous energy supply. The relative position changes between the energy transmiting coil and the receiving coil of the inspection robotic fish significantly affects charging efficiency and power. Effectively identifying the mutual inductance value and the equivalent load resistance value between the coils has become one of the key issues in improving the energy efficiency characteristics of the system. To address such challenges， this paper proposes an online mutual inductance and load identification method for wireless charging systems of inspection robotic fish， based on a hybrid compensation topology. By detecting input impedance changes before and after topology switching， this method accurately identifies mutual inductance and load resistance， enabling optimal compensation topology selection to improve system energy efficiency characteristics.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
In response to the challenges posed by complex microhole backgrounds in frames， a multitude of small and medium-sized defective targets， and the high degree of shape randomness encountered in mobile phone visual inspections， we have developed an enhanced YOLOv8-burr model based on YOLOv8 model. This model incorporates a lightweight global attention transformation module， which leverages packet convolution， within the network neck region. It also integrates a multi-scale feature extraction module into the backbone and employs a polarization self-attention mechanism along with a CARAFE operator in the network sampling stage. These innovations enable the model to harness global feature information and multi-layer channel details for more precise detection of small target defects. The experimental results show that the improved model has a size of 14.4M and can achieve 92.1% accuracy of microhole defect recognition， and in the category of "burr" defects that are difficult to identify， the accuracy has been improved by 10.4% compared with the original model before improved， which meets the identification accuracy requirements of the robot for the identification of microhole machining defects in the mobile phone frame.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
The traditional artificial potential field method does not consider the dynamic constraints of vehicles during path planning. By integrating a model predictive control system at the planning layer， it is possible to achieve trajectory replanning for obstacle avoidance； however， the generated trajectories are often not smooth enough， leading to poor vehicle stability. To address these issues， we first improved the classic artificial potential field repulsive model by introducing the concept of potential field smoothing index. We studied the impact of different potential field smoothing indices on the repulsive model and considered the influence of vehicle velocity on obstacle avoidance within the repulsive model. Secondly， we constructed a comprehensive potential field model that includes road boundaries， traversable obstacles， and non-traversable obstacles. This comprehensive potential field model serves as the obstacle avoidance function in the planning layer of the model predictive algorithm， and we designed an APF-MPC controller capable of classifying and avoiding different types of obstacles. Finally， through joint simulations with CarSim and Simulink， we analyzed the effects of the potential field smoothing index on the generated path， front wheel steering angle， yaw angle， slip ratio， and front wheel longitudinal force of the vehicle under mixed obstacle scenarios. The research results indicate that the designed APF-MPC controller can classify and avoid different obstacles， exhibits strong robustness， generates smoother paths， and enhances the stability and safety of the vehicle.