In order to meet the operation requirements of the Tianjin trunk line water conveyance box culvert of the South to North Water Transfer Project, a special multifunctional operation vehicle is developed and its high precision control is realized. Firstly, the structure and working mechanism of the multifunctional operation vehicle are described, and its kinematics models are established respectively. Secondly, a cross coupling synchronization control strategy is proposed to solve the problem of vehicle body attitude deviation caused by speed non-synchronization in dual motor independent control. Finally, the system model of multifunctional vehicle is built and the simulation analysis of cross coupling synchronization control is completed. The results of software simulation and system engineering application show that the proposed cross coupling synchronous control strategy can effectively improve the non-synchronization errors of double motors and improve the attitude control accuracy of the multifunctional vehicle for box culverts, and has strong engineering practicability and application popularization.

By predicting the wear trend of aeroengine, the wear state of aeroengine can be monitored effectively. Among the effective observation data reflecting the engine wear state, the oil analysis data can indirectly reflect the overall wear trend of aeroengine. Therefore, by establishing a trend prediction model based on oil sample analysis data, so as to realize the wear trend prediction of engine. However, the current models used in aeroengine trend prediction are mainly single prediction models, and the combined prediction models are only general linear combinations, with poor prediction effect. Therefore, a nonlinear variable weight combination prediction model based on support vector machine is proposed, and realizes the parameter optimization through particle swarm optimization algorithm. The oil sample analysis data is obtained through the bearing fatigue test of the whole life oil system, and the oil samples are collected at fixed intervals for performance analysis. Through the combination prediction analysis of the spectral analysis data, by comparing the prediction results of the combination prediction and the prediction results of the single prediction model, the prediction accuracy exceeds the prediction accuracy of the single prediction model, which fully verifies the superiority and effectiveness of the combination prediction model proposed in this paper.

Based on the stress model of excavator working device, a pin dynamic load test method considering eccentric load and side load of excavator working device is proposed. According to the stress characteristics of the pin shaft at the articulated hole between the bucket and the stick, a pin shaft load test sensor is designed to measure the dynamic load in the horizontal, vertical and lateral directions at the articulated point, and measure the displacement of the three oil cylinders of the excavator at the same time. A dynamic test system for the pin load of the excavator working device was established. Taking the domestic 50 t excavator as the prototype and the stonework as the working material, the excavation simulation loading test was carried out. The results show that the proposed pin load test method can accurately obtain the three-dimensional dynamic load of the pin at the joint of the bucket and the bucket. The maximum load occurs in the excavation section. The lateral load is negligible compared with the normal load. The results of the study provide the basis for the structural load spectrum test and fatigue optimization design of excavator.

In order to explore the safety threat of slope deformation to transmission lines, taking the transmission tower-line system as the research object, the overall finite element model of the tower-line system considering soil-structure interaction (SSI) effect is established, and the rationality of the numerical analysis model is verified based on the field measured data. On this basis, considering the relationship between the slope deformation area and the spatial position of the tower and the influence of the slope deformation angle, the response law of the stress characteristics of the tower-line system to the slope deformation is explored. The results show that when the towers are located above, below and inside the slope deformation body respectively, the failure mode and deformation resistance of the upper tower line system are significantly different. And with the increase of slope deformation angle, compared with horizontal slope deformation, the anti-deformation ability of tower-line system will decrease by 25%-50%. When the tower is located outside the slope deformation body, the anti-deformation ability of tower-line system will decrease most seriously, with the decrease range of 33%-50%.

During the operation of an air cushion belt conveyor, factors such as fan air volume, number of air holes, and film thickness have a significant impact on the flow field characteristics and bearing capacity of the air cushion. By establishing a simulation model for the air cushion flow field, analyze the changes in air cushion pressure under different working conditions. Based on experimental comparison and analysis, the variation law of air cushion pressure under different air volume and number of air holes, as well as the variation of air film thickness and air film pressure under different air volume were analyzed. The results show that as the air volume increases, the film pressure gradually increases, and the film thickness changes from 5% to 30%. As the number of pores increases, the pressure gradient of the air cushion changes faster and exhibits a parabolic distribution, and the degree of change in air film thickness decreases. The experimental and simulation results show that the changes in the air cushion flow field are consistent, and the fan air volume is 15-20 m³/m, the optimal value K₁ for the stability of the air cushion flow field is 1.31, and the optimal working condition is 5 exhaust holes, which meets the requirements of actual operating conditions.

To thoroughly explore the extensive applications and prospects of knowledge graphs in the domain of intelligent manufacturing, aiming to support the sustained development of the manufacturing industry, the domain of intelligent manufacturing has been categorized into four dimensions: vertical industry applications, manufacturing process applications, domain graph construction technology, and intelligent services. Through this study, the significance of knowledge graphs in driving the evolution of intelligent manufacturing is reviewed. Furthermore, a framework for an intelligent manufacturing knowledge graph, rooted in manufacturing domain knowledge data, is proposed. This framework encompasses three key modules: manufacturing domain data, graph construction, and intelligent services, providing theoretical support for the continuous upgrading of intelligent manufacturing. Research findings emphasize the crucial role of knowledge graphs in advancing the intelligence of the manufacturing industry and the broad potential of knowledge graphs in the field of intelligent manufacturing. Additionally, an outlook on future research directions for knowledge graphs in the domain of intelligent manufacturing is suggested, with a focus on exploring the integration of domain graphs with next-generation artificial intelligence technologies to propel the continuous innovation and intelligent evolution of manufacturing.

In recent years, intelligent control technology has occupied an increasingly significant position in the field of modern engineering research. With the rise of artificial intelligence technology, it provides more possibilities for construction machinery to realize intelligent control. In the development process of intelligent technology, the control accuracy of construction machinery system is improved, industrial production is more reliable and safe, and the production efficiency of enterprises is improved. This paper starts from the content of intelligent control technology, introduces various intelligent control technology theories and methods, and discusses the application of intelligent control technology in various construction machinery according to intelligent technology methods. At the same time, the key problems and technical system of future construction machinery under intelligent control are analyzed and studied, which provides reference for the intelligent development of construction machinery control technology.

In order to solve the problem that the trajectory is not straight when piecewise polynomial interpolation method is used to plan the trajectory of excavator's linear operation in joint space, which leads to the inaccurate trajectory, this paper proposes a quintic polynomial interpolation method based on multidimensional trajectory to realize the accurate trajectory of excavator's linear operation. While using Matlab to draw the relevant images in motion, ikunc function is used to optimize the inverse solution of each point in the motion process, and joint Angle trajectory smoothing algorithm and particle swarm optimization algorithm are used to verify the minimum change of joint Angle in the optimal inverse solution of ikunc function. It is verified that the ikunc function is correct to obtain the minimum and best inverse solution of the joint Angle change, which indirectly achieves the energy optimal effect.

A theoretical model of stress distribution and contact width is established by Hertz contact theory. The reaction of roller under specific load is analyzed. The results shows that linear profile have significant edge contact stress concentration, which increases with the increase of load. The roller with logarithmic profile can solve the stress concentration, but it can't completely eliminate it. Under the same load, with the increase of the bias factor, the maximum contact stress of the roller decreases sharply at first and then increases slowly. The position where the maximum contact stress occurs shifts from both ends of the roller to the middle of the roller. The profile design of roller with logarithmic type can take a value between 1.0 and 2.5 for the bias factor based on working conditions, roller size and load conditions. The value of bias factor is adaptive to tilt error and the impact of tilt error on contact stress can be reduced by setting a reasonable value.

In the railway container yard, there are few mature intelligent anti-lifting solutions available for train flatbed loading and unloading operations due to the poor detection accuracy or speed of traditional detection methods. This paper proposes a fast anti-lifting detection method for trains based on an improved back propagation (BP) neural network. By acquiring weight data from the four locks of the hoist, a flatbed lifting detection model is established using a BP neural network. During weight adjustment, a momentum factor and an adaptive learning rate are incorporated to optimize the model's performance. Through practical tests, this method demonstrates that this model achieves a high detection rate and fast detection speed, making it suitable for providing intelligent safety protection for automated rail mounted gantry in the railway container yard.