Search

Advanced airborne equipment liquid cooling system presents the development trend of multiple pumps in parallel, centralised heat dissipation and high efficiency circulation, but this type of system has the characteristics of many variable parameters and strong non-linearity, which makes the system operation and control become extraordinarily complex, posing a serious challenge to the system performance. Although some progress has been made in the current optimization of liquid cooling system performance, there are still gaps such as insufficient consideration of the multi-objective optimization problem and limited optimization effect. To fill this research gap, this study takes minimum specific energy consumption, minimum flow deviation and minimum impeller load as the optimization objectives, establishment of multi-objective optimization model for liquid-cooled power unit, the optimal solution set of the Pareto model is solved using the multi-objective grey wolf optimization algorithm (MOGWO), and the optimal solution set is substituted for the optimal solution, and the evaluation indexes are designed in combination with the actual working conditions to obtain the optimal schedule. By example analysis, the optimized system under variable operating conditions, the impeller load and specific energy consumption decreased by 16.88% and 3.64% respectively, while the rise in flow deviation was controlled within 0.76%. The newly established optimisation model effectively mitigates potential safety hazards during system operation. Finally, by combining all the indicators, three scheduling options are provided under variable operating conditions, focusing on reliability and economy, overall balance and high-precision flow, respectively, in order to adapt to the diversified requirements of different operating environments.

When a high-speed rolling bearing cage comes into contact with rollers, it experiences random movement due to friction and collision, which significantly impacts the overall performance of the bearing. To further investigate the motion law of cages, a test bench for a 7010C angular-contact bearing cage was constructed. This setup utilized laser sensors to obtain changes in attitude and displacement during operation. After analyzing how cage deflection errors influenced trajectory measurements, corrections were applied to the measurement results. Additionally, an investigation was conducted into the effects of varying rotational speeds on the dynamic performance of the cage. Simulations were performed using ADAMS software, which verified both the effectiveness of the measuring method and the testing results. The findings indicated that within the tested range of rotational speeds, the centroid trajectory stability of the cage gradually improved as rotational speed increased and then began to show a tendency to deteriorate. Furthermore, there existed a negative correlation between the deflection error of the cage and the centroid trajectory stability.

Robots need to sense information about the external environment before moving, which helps them to recognize and understand their surroundings so that they can plan safe and effective paths and avoid obstacles. Conventional algorithms using a single sensor cannot obtain enough information and lack real-time capabilities. To solve these problems, we propose an information perception algorithm with vision as the core and the fusion of LiDAR. Regarding vision, we propose the YOLO-SCG model, which is able to detect objects faster and more accurately. When processing point clouds, we integrate the detection results of vision for local clustering, improving both the processing speed of the point cloud and the detection effectiveness. Experiments verify that our proposed YOLO-SCG algorithm improves accuracy by 4.06% and detection speed by 7.81% compared to YOLOv9, and our algorithm excels in distinguishing different objects in the clustering of point clouds.

To address the limitations of the Deep Deterministic Policy Gradient (DDPG) in robot path planning, we propose an improved DDPG method that integrates kinematic analysis and D* algorithm, termed D*-KDDPG. Firstly, the current work promotes the reward function of DDPG to account for the robot’s kinematic characteristics and environment perception ability. Secondly, informed by the global path information provided by the D* algorithm, DDPG successfully avoids getting trapped in local optima within complex environments. Finally, a comprehensive set of simulation experiments is carried out to investigate the effectiveness of D*-KDDPG within various environments. Simulation results indicate that D*-KDDPG completes strategy learning within only 26.7% of the training steps required by the original DDPG, retrieving enhanced navigation performance and promoting safety. D*-KDDPG outperforms D*-DWA with better obstacle avoidance performance in dynamic environments. Despite a 1.8% longer path, D*-KDDPG reduces navigation time by 16.2%, increases safety distance by 72.1%, and produces smoother paths.

In response to the challenge of timely fault identification in the spindle bearings of machine tools operating in complex environments, this study proposes a method based on a combination of infrared imaging with an Informer and a CNN + Swin Transformer. The aim is to achieve real-time monitoring of bearing faults, precise fault localization, and classification of fault severity. To accomplish this, an angular contact ball bearing was chosen as the research subject. Initially, an infrared image dataset was constructed, encompassing various fault positions and degrees, by simulating different forms of bearing faults. Subsequently, an Informer-based bearing temperature prediction model was established to select faulty bearing data. Lastly, the faulty data were input into the CNN + Swin Transformer model for bearing fault recognition and classification. The results demonstrate that the Informer model accurately identifies abnormal temperature rises during bearing operation, effectively screening out faulty bearings. Under steady-state conditions, the model achieves a classification accuracy of 97.8%. Furthermore, after employing the Informer screening process, the proposed model exhibits a recognition precision of 98.9%, surpassing other models such as CNN, SVM, and Swin Transformer, which are mentioned in this paper.

Ultrasonic surface rolling process (USRP) has the potential to improve the surface mechanical properties of metal components with platelike or cylindrical macrostructure, but its effect on spherical surfaces remains to be studied in depth. In order to investigate the effect of USRP on the surface roughness, hardness and wear resistance of a spherical joint bearing made of GCr15 bearing steel, ultrasonic rolling strengthening was carried out on a spherical bearing surface under various conditions. The surface roughness and hardness variations of samples before and after strengthening were investigated. It was found that the USRP strengthening process can effectively enhance the surface properties of GCr15 spherical bearing materials, reduce the surface roughness by more than 45%, and increase the surface hardness by more than 10%. Friction and wear tests were carried out before and after ultrasonic rolling. The results show that the friction coefficient of the bearing surface can be reduced by 28%, and that the wear volume can be reduced by 29%. The variation in the friction coefficient correlated to the variance of wear volume as the reinforcement changes.

Traditional aero-engine bearings rotate simultaneously with their inner and outer rings, which makes the temperature rise prediction model computationally large with low accuracy, and it cannot be accurately verified due to the means of testing. This paper presents a method for predicting the temperature rise characteristics of aero-engine bearings under composite load conditions. Firstly, the local method is used to calculate the heat generation from heat sources such as bearing spin, lubricant drag, and the differential sliding of steel ball and collar, respectively, then finite element modelling and steady-state thermal analysis are carried out for aero-engine bearings under the simultaneous action of axial and radial external loads, a double-rotor test setup is designed and the predictive model is validated, and finally, the influences of rotational speed and load on the temperature rise characteristics of the bearings are investigated. The study shows that the aero-engine bearing prediction model proposed in this paper has high accuracy; with the increase in the rotational speed of the inner ring of the bearing, the temperatures of both the inner and outer rings of the bearing increase significantly; the temperatures of the inner and outer rings of the bearing increase with the increase in the axial load, and the effect of the radial load on the temperature of the bearing is not obvious.

Temperature has a great influence on the stability of bearing performance. For the study of bearing steady-state temperature, this paper proposes a test method to quickly predict the steady-state temperature of the outer ring of a bearing, which solves the problems in traditional theoretical calculations and simulation analysis methods such as the large number of calculations, complex models, and large errors. Firstly, a mathematical prediction model is established according to the bearing temperature-rise law; then, a bearing steady-state temperature detection device is designed; and finally, the prediction model parameters are solved according to the experimental results, and experimental verification is carried out. It is shown that the prediction model has high accuracy under different load and speed conditions, and the error between the predicted steady-state temperature and the tested steady-state temperature is less than 0.7 °C. This prediction method reduces the single test time of the same speed to 60 min, which greatly improves the efficiency of the temperature detection test. The steady-state temperature model has important theoretical significance in guiding the study of the limiting speed of bearings.

The temperature of the molten pool in Laser Solid Forming has a direct effect on the dimensional accuracy and mechanical properties of the parts. Accurate prediction of the melt pool temperature is important to ensure the stability of the melt pool temperature and to improve the forming accuracy and quality of the part. In order to accurately predict the melt pool temperature, this study proposes a melt pool temperature prediction method based on particle swarm optimization (PSO) optimised long short-term memory neural network (LSTM). Using IR camera to obtain melt pool temperature data and establish long short-term memory neural network melt pool temperature prediction model based on experimental data. Optimization of the initial learning rate and the number of hidden layer units of the long short-term memory neural network model using the particle swarm optimization algorithm to build a PSO-LSTM model for prediction of melt pool temperature. The results show that the PSO-LSTM prediction model outperforms the long short-term memory neural network and Ridge Regression models in all evaluation indicators and can achieve accurate prediction of melt pool temperature.

This study addressed the issues related to the difficulty of determining the operating status of machine tool spindle bearings due to the high rotational speeds and rapid temperature fluctuations. This paper presents an optimized model that combines Convolutional Neural Networks (CNNs) and Informer to dynamically predict the temperature rise process of bearings. Taking the H7006C angular contact ball bearing as the research object, a combination of experimental data and simulations was used to obtain the training dataset. Next, a model for predicting the temperature rise of the bearing was constructed using CNN + Informer and the structural parameters were optimized. Finally, the model’s generalization ability was then verified by predicting the bearing temperature rise process under various working conditions. The results show that the error of the simulation data source model was less than 1 °C at steady state; the temperature error of the bearing temperature rise prediction model was less than 0.5 °C at both the temperature rise and steady-state stages under variable rotational speeds and variable load conditions compared to Informer and Long Short Term Memory (LSTM) models; the maximum prediction error of the operating conditions outside the dataset was less than 0.5 °C, and the temperature rise prediction model has a high accuracy, robustness, and generalization capability.

During operation, angular contact ball bearings exhibit slipping behavior, which accelerates bearing wear and reduces the bearing’s overall service life. This paper focuses on studying the degree of slippage and the variation in slippage rate in the H7006C angular contact ball bearing under constant inner ring speed and angular acceleration conditions. To achieve this objective, the study defines the contact between rolling elements, inner and outer rings, and the cage as flexible. A rigid–flexible coupling slippage dynamic simulation model is then developed, which utilizes cage slippage rate as the evaluation index. The analysis method of cage slippage rate change is established through the Gaussian function fitting method. By comparing simulation and testing results, the effectiveness of the analysis method of angular contact ball bearing slip behavior is validated. On this basis, the influence law of bearing speed and acceleration conditions on the slipping behavior of angular contact ball bearings is further studied. The results of the study show that as the axial load increases at different inner ring speeds, the cage speed increases until it reaches a stable state, while the cage slip rate continuously decreases until reaching a stable state. The higher the inner ring speed, the greater the axial load required for the bearing to enter the steady state. Under different angular accelerations, the cage speed increases with time; when the cage speed increases to stable and constant, the cage slip rate decreases to a stable state, and the higher the angular acceleration, the longer it takes for the bearing to enter the stable state.

The operating environment of rolling bearings in mine hoists is complicated, and detecting their faults is hindered by a weak and unstable initial vibration signal. This directly affects the ability to extract pertinent fault features. This paper puts forward an adaptive fault diagnosis method for rolling bearings that combines the Variational Modal Decomposition (VMD) model and Vision Transformer (ViT) deep learning network model. The objective was to address the difficulty of extracting relevant fault features from bearing vibration signals in environments with strong noise levels. First, an improved VMD+ViT model was used to remove the strong noise from the original bearing signal and adaptively classify the fault types; then, the impacts of modal components and encoder numbers on the accuracy of fault diagnosis were explored. Finally, the proposed methodology was validated by applying it to actual rolling bearing fault data, including both open-source and fault test datasets. The research findings indicated that employing a VMD+ViT integrated model consisting of one modal component with the highest Pearson correlation coefficient and eight encoders resulted in high accuracy in diagnosing faults, even in the presence of high levels of noise in the bearing’s vibration signal. The proposed diagnostic method achieved a diagnostic accuracy of over 92.70% on the open-source bearing dataset with strong interference noise and over 98.62% on the fault test dataset. The proposed method exhibited high accuracy and strong robustness, making it suitable for effectively diagnosing and accurately identifying different categories of rolling bearing faults in mine hoists, even in environments with high levels of noise.

Aiming to solve the problem of oil-air lubrication failure caused by the high working temperature of high-speed rolling bearings, this study proposes a method, based on the theory of gas-solid two-phase flow and bearing tribology, of predicting the dynamic temperature rise of nonlinear high-speed rolling bearings under oil-air lubrication conditions. The accuracy of the fluid–structure coupling model is verified by comparing the temperature rise test results of angular contact ball bearing at different speeds. The characteristics of oil-air lubrication circulation and the relationship between the lubrication parameters and the heat balance of the high-speed rolling bearings have been studied. The results show that the gas supply pressure of the system has a significant influence on the continuity and fluctuation of the oil film in the oil pipe nozzle. The initial rise in temperature of the inner and outer rings of the bearing and the fluid domain has a speed threshold value, and the temperature increases linearly with the bearing speed. With the increase in the oil supply and lube oil viscosity of the system, the temperature rise of the outer ring of the bearing increases first, then decreases, and finally increases again. There is an optimal oil supply 5.5 mL and optimize viscosity 68 cSt for the bearing in the work condition.

Various navigation tasks involving dynamic scenarios require mobile robots to meet the requirements of a high planning success rate, fast planning, dynamic obstacle avoidance, and shortest path. PRM (probabilistic roadmap method), as one of the classical path planning methods, is characterized by simple principles, probabilistic completeness, fast planning speed, and the formation of asymptotically optimal paths, but has poor performance in dynamic obstacle avoidance. In this study, we use the idea of hierarchical planning to improve the dynamic obstacle avoidance performance of PRM by introducing D* into the network construction and planning process of PRM. To demonstrate the feasibility of the proposed method, we conducted simulation experiments using the proposed PRM-D* (probabilistic roadmap method and D*) method for maps of different complexity and compared the results with those obtained by classical methods such as SPARS2 (improving sparse roadmap spanners). The experiments demonstrate that our method is non-optimal in terms of path length but second only to graph search methods; it outperforms other methods in static planning, with an average planning time of less than 1 s, and in terms of the dynamic planning speed, our method is two orders of magnitude faster than the SPARS2 method, with a single dynamic planning time of less than 0.02 s. Finally, we deployed the proposed PRM-D* algorithm on a real vehicle for experimental validation. The experimental results show that the proposed method was able to perform the navigation task in a real-world scenario.

The characteristics of rope vibration of the winding mine hoist are one of the main evaluation indices for evaluating the characteristics of the engineering mechanics and the safety coefficient of the hoist system. For this purpose, research was conducted on the vision-based measurement method for catenary transverse vibration. Firstly, a high-speed camera was utilized to capture grayscale images of the catenary transverse vibration. Secondly, the region of interest (ROI) was selected, and the ROI was binarized based on the wavelet edge extraction algorithm. Thirdly, in the ROI, the centroid position of the rope was obtained by calculating the matrix, which was described by the binary, and later, the transverse vibration signal of the catenary was obtained. Finally, the empirical mode decomposition (EMD) method was used to remove the periodic excitation caused by the displacement of the catenary winding. The experimental results show that the proposed method quickly and accurately comprehends the noncontact measurement of the catenary transverse vibration, and this method could also provide the technical support necessary for the measurement and evaluation of the safety parameters of the hoisting system.

Seedling transplanting is an important part of vegetable mechanized production in modern agriculture. After the seedlings are cultivated on a large scale by the nursery tray, they are planted into the field by the transplanter. However, unlike manual transplanting, transplanter is unable to judge the status of seedlings in the hole during seedling planting, which leads to problems such as damaged seedlings and empty holes being picked in the same order and planted into the field, resulting in yield reduction and missed planting. Aiming at this problem, we designed a seedling selective planting control system for vegetable transplanter which includes vision unit, seedling picking mechanism, seedling feeding mechanism, planting mechanism, pneumatic push rod unit, limit sensor, industrial computer and logic controller. We used asymmetrical light to construct visual identification scenes for planting conditions, which suppresses environmental disturbances. Based on the intersection operation of mask and image, a fast framework of tray hole location and seedling identification (FHLSI) was proposed combined with FCM segmentation algorithm. The vision unit provides the transplanting system with information on the status of the holes to be transplanted. Based on the information, planting system chooses the healthy seedlings for transplanting, improving the survival rate and quality of transplanting. The results show that the proposed visual method has an average accuracy of 92.35% for identification with the selective planting control system of seedlings and improves the transplanting quality by 15.4%.

The most usual failure mode of any mechanical structure is fatigue, which is characterized by an important feature of the decrease of elastic modulus of the material. In this paper, a fatigue life evaluation model based on equivalent elastic modulus is proposed for in-service mechanical structure. In the proposed model, parameters that represent the operating conditions of the mechanical structure, such as load, vibration, and shaft torque, etc., are used as the generalized load. To replace the fatigue stress, the statistical method is used here, which is also used in the conventional fatigue analysis method. The structural strain is also measured simultaneously. Using the statistical theory, the equivalent modulus of elasticity is formulated based on the relationship of stress, strain, and modulus of elasticity. To validate the proposed model, an online fatigue damage experiment has been conducted. The experimental results have been compared with that of the fatigue life prediction model with good agreement. It is expected that the methodology proposed in this paper will be widely used.

The properties of Physical Vacuum Deposit (PVD) films could be improved by doping rare earth elements and other metal elements. In this paper, La-Ti/WS _2 composite films were prepared by unbalanced magnetron sputtering. The effect of target power on the structure and properties of La-Ti/WS _2 composite films was studied. Scanning electron microscopy (SEM) and x-ray diffraction (XRD) were used to analyze the micro morphology, composition and crystal structure of the film. The hardness and friction and wear properties of the film were tested by nano-indentation and friction and wear testing machine. The results show that the structure and composition of La-Ti/WS _2 composite films prepared by magnetron sputtering are affected by the target power. With the increase of the target power, the diffraction peak of WS _2 (002) was shifted to the low θ value, the crystal surface spacing was decreased, the crystal lattice shrinked, the porous structure of La-Ti/WS _2 composite films was decreased significantly, the coarse columnar crystal was refined significantly, and the film density was improved effectively. When the target power is 20 W, the sliding surface of La-Ti/WS _2 composite film (002) is parallel to the surface, showing lower friction coefficient and better wear resistance; when the target power is 50 W, La-Ti/WS _2 composite film has higher hardness, higher wear rate and poor wear resistance. The density and friction and wear properties of La-Ti/WS _2 composite films can be improved by suitable La-Ti content.