When the hook mass or the cable length between the load and the hook becomes non-negligible， the double pendulum effect that may cause on the bridge crane can lead to the performance degradation of all control methods based on the single pendulum assumption. For this reason， an adaptive bounded tracking control method based on trajectory planning for double-swing bridge cranes is proposed in this paper. Firstly， an S-shaped curve is planned as a displacement reference trajectory， and a swing suppression link is introduced into the trajectory to define a new coupled system localization error term. After that， a new energy storage function is constructed based on the total energy of the system， and a swing suppression equation is derived and established on this basis to design the adaptive tracking controller. The introduced error constraint function ensures that the system coupled tracking error is always within the set upper and lower boundary conditions. Finally， the stability of the system is proved using Barbalat's theorem and Lyapunov's method. A series of simulations and experiments show that the method can not only accurately drive the cart to reach the specified position， but also effectively inhibit the oscillation of the load and the hook， and is robust to the parameter changes of the bridge crane and external disturbances.

In this paper， a high-frequency electromagnetic noise suppression strategy for automotive electronic water pumps is proposed based on an improved random carrier space vector pulse width modulation （RCSVPWM） method. Firstly， a new random sequence generator is designed with the Xorshift algorithm as the core to generate random numbers with better uniformity to increase the weakening effect on the high-frequency harmonic amplitude. Secondly， a sawtooth wave periodic function is combined to disperse and concentrate a large number of harmonics appearing at the carrier frequency and its integer multiples. Thereafter， a multi-platform simulation model of automotive electronic water pump is built to compare and analyse the harmonic suppression effects of SVPWM， RCSVPWM and the improved RCSVPWM control strategy， and to verify the suppression ability of the improved RCSVPWM control strategy on high-frequency harmonics. Finally， the experimental platform of the automotive electronic water pump is built， where the operation results of the electronic water pump under the three control strategies are mutually verified with the simulation results. The harmonic expansion factor is reduced by 2.35， and the suppression effect of high-frequency harmonic amplitude is improved by 10.39%. It is verified that the improved RCSVPWM control strategy can significantly improve the high-frequency electromagnetic noise of the automotive electronic water pump without affecting the original control system.

A modified Smith-IFT control method is proposed to tackle the problem of model uncertainty or disturbances in the Smith compensation control method for first-order time-delay systems in industrial processes. The Smith controller C_{\mathrm{1}} is first designed by optimizing the {\mathrm{H}}_{\mathrm{\infty }} norm of the sensitivity function using the process model to enhance the output performance of system. Meanwhile， The iterative feedback tuning （IFT） method is adopted to design the data-driven controller C_{\mathrm{2}} and update the estimated model and controller C_{\mathrm{1}} to reduce the influence of model uncertainty or disturbances on the system. Taking pulp concentration control in an actual industrial process as an example， the simulations verify the effectiveness of the proposed method. Compared to the other two methods， the modified Smith-IFT control method has advantages such as small overshoot， short settling time and strong robustness.

The traditional direct torque control （DTC） tends to be susceptible to some factors such as speed overshoot， large torque ripple and poor anti-interference during the startup and the sudden load of the heavy duty AGV （automated guided vehical）. In order to improve the control performance of direct torque control for heavy-duty motors， a direct torque control strategy was designed based on the linear active disturbance rejection combined with improved sliding mode control. Firstly， an improved linear active disturbance rejection controller was adopted to replace the PI control structure to address the instability of speed tracking in the traditional PI control speed loop. Secondly， an original synovial controller was improved to tackle the problem of large torque ripple in the torque loop under heavy load. Finally， the control model was built by Matlab/Simulink， and the improved fusion control strategy was verified to reduce the torque and flux fluctuation of heavy-duty AGV motor by 56.12% and 21.8%， respectively. The speed fluctuation under sudden load was reduced by 50.2%， and the speed overshoot at start-up was eliminated， all of which improved the ride comfort of heavy-duty AGV.

Aircraft surface damage is one of the important hidden dangers threatening the flight safety. In order to ensure the continuous airworthiness， airline and periodic inspections of aircraft surface are required. At present， most of the inspection tasks still rely on manual visual inspections， which， however， does create problems such as low operational efficiency， poor safety， difficulties in ensuring inspection coverage ratio， strong subjectivity， misdiagnosis and missed detections. To solve these problems， an intelligent UGV （Unmanned Ground Vehicle） system that can automatically collect fuselage surface images is designed， and a CPP （Coverage Path Planning） algorithm that can cover a specified area is particularly proposed. The UGV is equipped with the functions of autonomous map building， localization， global path/local trajectory planning， and automatic control of elevation height and PTZ（Pan-Tilt-Zoom Camera）， etc. According to the planning results of CPP for UGV position， elevation height and PTZ， the whole system is able to operate automatically. The CPP algorithm is designed to satisfy the photographic constraints such as shooting distance， shooting inclination and overlap rate， while the acquired aircraft skin images can be used for image stitching and damage identification. In order to solve the problem of high center of mass caused by the introduction of elevation device， the CPP algorithm adopts the viewpoint projection merging strategy and designs a safe and stable motion mode. The simulation and experimental results show the effectiveness of the proposed scheme， which can not only realize the full coverage of the designated fuselage area but also ensure the quality of the captured images.

Gripping and handling robots are widely used in manufacturing sector. In order to mimic the bending envelope grasping behavior of elephant trunks in nature， a modular elephant trunk-imitating robot based on the combined drive of cable and shape memory alloy springs was designed. The structural design of the elephant trunk robot and the bending deformation regulation mechanism were described. The positive kinematic model of the elephant trunk robot was established based on the D-H method， and the Monte Carlo method was used to derive the workspace of the robot. The kinematic simulation analysis of the elephant trunk robot was carried out by using Adams， adjusting the size of the tensile spring stiffness between the modules， and verifying the feasibility of the elephant trunk robot as a holding mechanism through the simulation results of the opening and closing end tension angle. A prototype elephant trunk robot was built to complete bending experiments under different tension mappings of spring equivalent stiffness， and the polymorphic bending envelope performance of the elephant trunk robot was verified.

To address the issues of low efficiency， excessive redundancy， and the inability to dynamically avoid obstacles in indoor robot path planning when using the A^* algorithm， this paper proposes a fusion algorithm that combines an optimized A^* algorithm with the dynamic window approach. The proposed algorithm enhances the heuristic function by incorporating the distance from the parent node to the target node. It quantifies the obstacle information to dynamically adjust the weights of the heuristic function using an obstacle rate function. Additionally， it introduces a cost for turning to reduce unnecessary turns in the path and designs a strategy to remove redundant points， ensuring a globally optimal static path. It incorporates the offside angle， flexibly selects the key nodes of the A^* algorithm as local target points within the dynamic window to avoid the path getting trapped in local optima. The experimental results demonstrate that the improved fusion algorithm enhances the search efficiency， reduces the path length， resolves the issue of the dynamic window approach being trapped in local optima and enables the real-time obstacle avoidance.

Performing defect detection on power transmission and transformation equipment has become an important part of maintaining the stable operation of the power grid. Despite significant advancements in deep learning methods for defect detection in power equipment， there still exists challenges of a few-shot as the result of the scarcity of defect samples. To address this issue， a few-shot defect detection network for power transmission and transformation equipment is proposed based on meta-learning to improve the defect detection accuracy of power transmission and transformation equipment. The network utilizes DarkNet-53 as the backbone network of the detection framework and introduces multiple feature enhancement modules， including global information extraction， channel attention and cross-stage feature fusion to improve the backbone of the network and enhance the processing capability of data. By splitting the training set into support sets and query sets， a two-stage training process is conducted based on meta-learning algorithms. The meta-learning algorithm optimizes the parameter update strategy during the training stage to tackle the few-shot learning problem. The experimental results demonstrate that this method achieves a mean average precision （map） of 0.51 at IoU threshold 0.5， showing a significant improvement in the defect detection for power transmission and transformation equipment compared to the existing mainstream methods.

In order to improve the accuracy of fault diagnosis， a fault diagnosis model of coal mill is established on the basis of support vector machine （SVM）， while a fault diagnosis method based on whale algorithm （WOA） optimization support vector machine is proposed using the principal component analysis （PCA） feature selection. Firstly， the principal component analysis is used to extract features from the fault parameters of the coal mill to reduce the dimension of high-order raw data and to improve the efficiency of data classification and integration. Secondly， the whale optimization algorithm is used to optimize the parameters of the support vector machine， obtain the optimal model parameters and construct a fault diagnosis model. At the same time， the comparison of the particle swarm algorithm with the genetic algorithm optimization model is conducted for experimental verification. The results show that the classification accuracy of the WOA-SVM model is the highest， which can realize the accurate diagnosis of coal mill system faults in a short time， providing a practical reference for coal mill fault diagnosis.

Detection on bolt loosening is crucial for the safety of the normal operation of mechanical equipment or mechanical structure. There are drawbacks existed in the current visual detection methods of bolt loosening such as low efficiency and poor practicability and also certain limitations in the detection of mechanical systems with complex bolt distribution. Based on this situation， a visual detection method of multi-directional bolt loosening is proposed. For the visible front orientation of the bolt， the parameters of the camera are first calibrated， and the image captured by the camera is then converted to the HSV color space after being preprocessed such as filtering and noise reduction， The threshold of the specific channel is adjusted to obtain the ROI region， and finally，the rectangular ROI region is fitted into a straight line by the least squares method， and the slope of the line is converted into the bolt loosening angle to realize the detection of loose angle in the range of 0~180°， with the maximum absolute error being 0.98°. For the visible orientation on the side of the bolt， the ROI area is obtained after the image is processed， and the minimum external rectangle of the ROI area is then obtained by the rotation jamming method， Thereafter， the width of the minimum external rectangle is detected and the pixel width value is converted into the actual width value of the marking belt， which is then integrated into the conversion model of the marking belt length and the rotation angle of the bolt to obtain the actual bolt rotation angle， achieving the loose angle detection in the range of 0~300°， with the maximum absolute error of 3.59°， the minimum measurement absolute error of 0.2° and the average error of 1.77°. It can realize the non-contact detection of bolt angle during the working process of mechanical system， improve the detection efficiency and indicates a strong engineering applicability.

Predictive maintenance reduces unnecessary maintenance by timely diagnosing or predicting the state of the device， and deep learning methods can realize the above process by mapping the device feature parameters to the state of the device. The wide variety of equipment and the predictive maintenance of equipment in multiple working conditions also place higher demands for deep learning methods， and a reasonable selection of deep learning methods thus becomes crucial. Therefore， the development process and the latest application of deep learning in predictive maintenance are discussed in this paper， with a view to further promote the application of predictive maintenance in more devices， to improve the accuracy of deep learning methods in predictive maintenance applications， and to envision the future development of deep learning methods in predictive maintenance.

In order to improve the efficiency of cargo space allocation in automated warehouse and ensure its safe and stable operation， a cargo space optimization model is established to improve the efficiency of warehouse entry and exit， shelf stability， cargo correlation and surplus value of goods. The analytic hierarchy process is used to transform the multi-objective problem into a single-objective problem. An improved imperialist competitive algorithm is proposed for this model. The algorithm combines the advantages of imperialist competitive algorithm and genetic algorithm and designs a dynamic adjustment revolution rate formula and a natural disaster operator to enhance the diversity of colonies and empires. The experimental results show that the improved imperial competition algorithm has better convergence and search range， and effectively solves the problem of location allocation of different scales. The accuracy and stability of the solution are better than those of particle swarm optimization， genetic algorithm and standard imperial competition algorithm. It provides a theoretical basis and practical reference for improving the competitiveness of fast-selling enterprises and the efficiency of warehouse entry and exit.

To reduce the impact of temporary order insertion on the maximum completion time and the delivery time of flexible job shop scheduling through batch scheduling of jobs， a mathematical model for flexible job shop dynamic scheduling considering batch production is first established. Second， a three-layer chromosome coding scheme based on machine， process and batch is proposed. Then， a rescheduling method is used for the order insertion event. Finally， three local search neighborhood operations and the addition of nondominated sorting genetic algorithm in the selection operator are proposed to improve the optimization ability of the Memetic algorithm， and the improved Memetic algorithm is used to solve the model. Through the comparison of 6×8 examples， the maximum completion time of batch scheduling is reduced by 28.03% compared with that of non-batch scheduling， and the value of the early/delayed penalty function is reduced by 26.62%. Batch scheduling can effectively reduce the impact of job processing on the maximum completion time and the delivery time， with the optimal batch quantity of job being 2~3 batches.

Accurate prediction and judgement of wind turbine operation status can provide the early warning of faults， maintain the stable operation of the wind turbine， realize the reasonable scheduling of wind power and guarantee the stability and the safety of power production. In this paper， a wind turbine state prediction method with multi-sensor data fusion is proposed. First， the processing and feature extraction methods of wind turbine multi-sensor data are studied， the multi-sensor data are cleaned by applying the quaternion method and Relief-F algorithm， and the multi-sensor data source is selected based on the data weights. Second， an information fusion algorithm based on BP Neural Network and D-S evidence theory is designed and verified in MATLAB， and the accuracy of wind turbine state prediction is 80.35% and 78.72% respectively. Next， based on the idea of two-layer fault-tolerant data fusion， the D-S evidence theory method is improved. The multi-sensor data fusion algorithm based on FTDF-TCR is designed and verified by applying the same sample dataset. Finally， as verified by experiments， the accuracy of wind turbine state prediction is 89.36%， an increase by 9.01% and 10.64% respectively， compared with that of the original algorithm， which shows that the accuracy of prediction has been effectively improved.

A scheduling energy-saving model aiming to minimize the completion time and the effect of total machine load on overall energy consumption is developed to reduce the impact of two dynamic disturbance events such as emergency order insertion and machine failure on energy consumption in the aluminum pot production workshops. To address the limitations of the Non-dominated Sorting Genetic Algorithm II （NSGA-II） in terms of search efficiency and Pareto solution quality for multi-objective optimization problems， an Improved Non-dominated Sorting Genetic Algorithm II （INSGA-II） is introduced. The INSGA-II algorithm incorporates double chain coding and fast non-dominated sorting techniques to enhance convergence. The initial Pareto solutions are considered as an external elite set， which is continuously updated through multiple cross mutations and multiple fast non-dominated sorting iterations to improve the quality of the Pareto solutions. The effectiveness and superiority of the INSGA-II algorithm are validated through examples and algorithm comparisons.

Cold chain transport is the process of keeping goods at low temperatures from one location to another throughout the supply chain， used mainly in food， pharmaceutical， biomedical and other industries to ensure the quality and safety of goods， and the reasonable planning of transport is thus crutial for reducing logistics costs and carbon emissions and improving transport efficiency. This paper establishes a mathematical model for the optimization of cold chain logistics transport paths， taking into account the distance between different customer points， service time window， cargo demand and other factors， in order to achieve the goal of minimizing the total cost of transport， including fixed costs， transport costs， refrigeration costs and so on. The model is then solved using the improved seagull algorithm， which is a bionics-based optimization algorithm that simulates the behavior of seagulls when foraging， and the algorithm has global search capability and adaptivity. During the implementation of the algorithm， this paper considers the iterative parameter setting and convergence control of the algorithm to avoid falling into the local optimal solution and to ensure the reliability and the efficiency of the experimental results. The effectiveness of the cold chain logistics path optimization method based on the improved seagull algorithm is verified through the application of experimental examples and the comparative analysis of different algorithms.

To address the issue of the active actuation used mostly by variable stiffness joints， a lower-extremity exoskeleton with passive variable stiffness is proposed. Firstly， a variable stiffness joint is designed based on the basic walking principle of the lower limbs， which can change the output stiffness of the joint according to the walking gait. Then the theoretical model of that joint is established to analyze the joint torque and stiffness properties. Finally， a human-machine coupling model is established for dynamics simulation， and the effect of different initial angles of the joint on the exoskeleton's torque and stiffness properties is studied to obtain the optimal initial angle of the joint. The results show that the optimal output torque of the joint is achieved at an initial joint angle of 2.5°， and also has the maximum range of stiffness variation， in which case the exoskeleton can carry more loads.

A hybrid strategy sparrow algorithm （BOSSA） is proposed to address the low search accuracy of the original sparrow search algorithm （SSA） and the low accuracy of the SSA-BP model for predicting multi factor corrosion of oil pipelines. An improved Circle chaotic map is used to initialize the population and increase the diversity of population initialization； The discoverer's position change formula is replaced by the position iteration formula that is combined with the whale algorithm to improve the overall search ability of the algorithm； The sine cosine algorithm is brought into the joiners’ formula to improve the algorithm's local search ability； A nonlinear decreasing strategy is introduced to control the number of vigilantes in the population to accelerate the convergence speed of the population， while some sparrows for cross mutation are selected to enhance population diversity. With four test functions being used for testing， the results show that the improved SSA algorithm （BOSSA） has better optimization ability and iteration speed compared to other algorithms. Taken a certain oil pipeline as an example， the neural network is optimized with the improved algorithm to predict the remaining strength of pipelines. The results show that the average error of the prediction results of the BOSSA-BPNN model is merely 2.21%， which is significantly higher than that of other models. It can provide reliable theoretical and technical support for the pipeline inspection and maintenance.

A pure torsional dynamics model is developed for RV gearboxes which are widely used in industrial robots. The model contains key factors affecting vibration， such as input shaft torsional stiffness， planetary gear and cycloid mesh stiffness and bearing support stiffness. On the basis of the dynamics model， the differential equations of the system dynamics are derived， and the intrinsic frequency and vibration pattern of the system are solved. Combined with the theoretical basis of modal analysis， the modal characteristics of the RV gearbox under various constraints such as free， bearing constraints and fixed constraints are investigated by using the finite element analysis method， and the simulation results of the frequency distributions and vibration characteristics of the RV gearbox under different constraints are obtained. The results show that the simulation results of the modal analysis of the RV gearbox are closer to the theoretical calculation results and more in line with the actual working condition under the meshing working condition of bearing constraint and fixed constraint.

The modeling of ship element template is traditionally collaborated between the ship designers who put forward requirements and the software developers who write codes. The lack of expertise in ship design among software developers and the lack of computer programming knowledge among designers， however， tend to make the communication between the two parties difficult. To address this issue， an automatic generation method of ship element template is developed by integrating the large language model （LLM） and the controlled natural language （CNL）. The instructions for modeling an element template can be easily described in natural language （NL）， and then converted into CNL clauses using LLM. Thereafter， the CNL requirements are converted into visual coding utilizing specially designed mapping rules. The experimental results of 200 typical templates of ship piping elements show that the proposed method has a high accuracy and can be used in real ship design. At the same time， the developed method can avoid the costly LLM fine-tuning. In addition， the entire process of the above method does not require the participation of programmers， which solves the communication difficulties between designers and programmers， and therefore can significantlt improve the modeling efficiency.

Traditional ant colony algorithms generally suffer from low convergence efficiency， insufficient global search capabilities， and a tendency to fall into local optima when dealing with large-scale complex scenarios. The method enhances the guidance and adaptability of the heuristic function to improve the algorithm's optimization ability. By utilizing a pseudo-random transfer strategy to improve state transition rules and dynamically adjusting the pheromone heuristic factor and the expected heuristic factor， the global search capability is enhanced， and the issue of local optima is avoided. At the same time， a multi-strategy approach is adopted to optimize the pheromone update mechanism， combined with an adaptive adjustment of the evaporation coefficient， significantly improving convergence efficiency and search performance. Additionally， through secondary optimization by removing redundant points， the path quality is further optimized. The simulation experiments on grid maps demonstrate that the ant colony algorithm， after multiple strategy improvements， achieves significant improvements in search ability and convergence speed， providing theoretical support for solving path planning problems for AGVs in complex environments.

To improve the stability of freight AGV， the response of AGV suspension system under random road excitation conditions was studied， and stability analysis of freight AGV was conducted. Firstly， based on the time domain and spatial domain variables and their derivative transformation relationships， a vehicle vibration coupled response model and a road excitation model were derived. Then， based on BP neural network optimization， orthogonal experiments were introduced to optimize the stability evaluation indicators， and the optimal vibration response combination scheme was obtained. Finally， simulations and experiments were conducted to analyze the impact of state changes on driving stability of freight AGVs under non-stationary excitation， verifying the rationality of the model and optimization. The general laws of AGV time domain and driving state changes were summarized， providing new ideas for studying AGV driving stability.

A reasonable inventory control strategy for spare parts has direct influence on the stable production and the economic benefit of manufacturing enterprises， especially on the spare parts of class A with large production influence and large consumption. This kind of spare parts tends to have a long lead time and complicated demand changes. Therefore， two multi-objective dynamic inventory control strategies considering cost， satisfaction rate and turnover rate are proposed based on future prediction and historical demand. The effect of the model is verified by the real data of one cigarette factory. It is proved that， compared with the enterprise’s existing strategy， the dynamic model based on future prediction has the best effect for the spare parts whose demand occurrence time is unstable and less， with the comprehensive performance of each index improved by 50.74%. For the spare parts whose demand occurrence time is relatively stable， the dynamic model based on historical demand is more advantageous with comprehensive performance of each index increased by 4.31%. Meanwhile， the sensitivity analysis of each target’s weight is also carried out. The analysis shows that the dynamic model based on future prediction should be prioritized when the importance of the fill rate increases， whereas the dynamic model based on historical demand should be considered when the enterprise is more concerned about the total cost and turnover rate. The research results provide important management guidance for manufacturing enterprises to choose appropriate inventory control strategy under different demand characteristics and different target requirements.

Most of the existing anti swing control methods of overhead crane are based on the design of single crane models. When the load is too large and needs to be transported by two cranes together， a closed kinematic chain is formed between the two cranes， which makes the coupling relationship more complex. The offset of the center of gravity of a large load will increase the difficulty of swing angle control. A nonlinear coupling anti swing controller is proposed to solve the problems of positioning accuracy and eccentric large load swing in the overhead crane system with double cranes. First of all， the dynamic model of eccentric center of gravity is established for the lifting system of dual trolley bridge crane， and the equation of large size load swing angle is derived； Then， based on the characteristic structure of the lifting system of the dual trolley bridge crane， the coupling signal containing the trolley displacement and the swing angle of the two ropes is constructed， and the nonlinear coupling anti swing controller is designed based on the energy method. The simulation results show that the controller can effectively suppress and eliminate the swing angles of the lifting rope and the load， and its control performance is superior to traditional control methods and has strong robustness.

To address the difficulty of realizing the accurate assessment of the complex operating conditions of an electric spindle with a single monitoring signal， an analytical method based on the improved D-S evidence theory is proposed using the entropy weight method. Based on different types of monitoring signals of the electric spindle， the feature indicators that can represent the operating state of the electric spindle are obtained by combining with multi-domain feature extraction， principal component analysis （PCA） algorithm and improved density-based spatial clustering of applications with noise （DBSCAN） algorithm. The concept of belief entropy is introduced， and the classical D-S evidence theory algorithm is improved based on the entropy weight method to reduce the interference of insensitive signals and improve the fusion effect of weak state information features. Besides， an electric spindle test bench is fabricated，in which the service state assessment effects of the electric spindle at different speeds are compared and studied. Relying on the existing integrated development environment （IDE）， the assessment system of operation and maintenance information management of the spindle is developed， and the application of data collection， health monitoring and state assessment during the service process of the electric spindle is realized， satisfying the engineering requirements of operation and maintenance of high-precision electric spindles. It provides a systematic solution for the state diagnosis and maintenance management for machine tool or equipment manufacturers.

CNC machine tool with high precision is an essential platform for manufacturing high value-added parts， exhibiting considerable repeatability and automaticity. However， chatter during manufacturing has seriously restricted the application of this technology. When suppressing the machining chatter， existing studies focus mainly on the high-frequency vibration on the tool-holder-spindle system， ignoring the low-frequency oscillation contributed by the machine tool. To address this issue， this paper investigates the manufacturing dynamic properties of machine tool by integrating the low-frequency mode shape analysis and the forced vibration simulation. Executing experimental modal analysis in low-frequency band of the machine tool to improve its structure， the original and modified structures are then processed by FEM to capture their modal parameters. Establishing structural dynamics models for the machine tools while simulating their forced vibrations， the outcomes are compared to observe their stabilities. The results show that the natural frequency and damping ratio of the new machine tool are all improved， whereas the vibration amplitude is reduced to 30% of the original situation， indicating that the proposed structure modification strategies of the machine tool have a significant effect on chatter suppression.

The flexible linkage mechanism driven by permanent magnet synchronous motor is a complex and strongly coupled nonlinear system， which is widely used in the applications with lightweight structure and high self-weight ratio， such as flexible manipulators， solar panels， and automated production lines. The existence of flexible links can produce obvious deformation， cause resonance， reduce the accuracy of the system and even lead to instability， all of which increases the difficulty of the system modeling and control. At the same time， since the model parameters of the flexible linkage system are difficult to fully obtain， a Multiple Capacity Process PI （MCP-PI） control method based on parameter identification of multiple information models is therefore proposed in this paper. Firstly， a dynamic model of the flexible linkage system is established according to its structural characteristics. Secondly， the model of the flexible linkage servo system is discretized， and the multi-innovation stochastic gradient algorithm is used to identify the parameters of the flexible linkage. Then， the multi-capacity process theory is used to design the PI control parameters of the current loop and speed loop. Finally， the rapidity and accuracy of the proposed method are demonstrated by numerical simulation， and the superiority of the proposed MCP-PI control method is verified by comparing it with engineering design method and pole placement method.

The fast charging and discharging characteristics of the flywheel energy storage devices （FESDs） make them very suitable for solving the problem of energy fluctuation caused by grid connection of renewable energy. Currently， most of the FESDs are small-capacity and light-load devices. Aiming at designing a device of 100 kWh， 10，000 r/min large-capacity heavy-duty FESD used for frequency modulation， the electromagnetic bearings which are the key rotor support part of the FESD are designed at first. Based on the requirement of load carrying capacity of 100 kWh heavy-duty flywheel rotor， both the radial and the axial electromagnetic bearings are designed respectively， the parameters of electromagnetic force and magnetic induction strength are simulated and verified， and the simulation analysis and verification are conducted on the load carrying capacity. Meanwhile， the intrinsic frequency and critical rotation speed of heavy-duty flywheel rotor under equivalent stiffness are investigated， and the influence of different support stiffness on the intrinsic frequency of heavy-duty flywheel rotor is analyzed. The Research results demonstrate that the designed heavy-duty electromagnetic bearings can satisfy the operation requirements of the 100 kWh large-capacity heavy-duty FESD.

Taking a certain type of vehicle decelerating permanent magnet starter as the research object， a permanent magnet DC motor of the starter is simulated and analyzed by RMxprt software， and the output performance of the starter at different temperatures is obtained， wherein the problems of insufficient power and low speed were found in the starter. The finite element analysis of the transient magnetic field of the starter shows that the magnetic field inside the starter is evenly distributed and with no magnetic leakage. By analyzing structural parameters such as air gap and permanent magnet， four electromagnetic parameters that have the greatest impact on starter performance are obtained. With the polar arc coefficient of permanent magnet， the thickness of permanent magnet， the rotor outside diameter and the rotor length taken as the optimization variables， and the speed and torque of the starter as the optimization objectives， the multi-objective optimization of starter is carried out by genetic algorithm through optiSLang software. Finally， the simulation and experimental verification of the optimized starter show that the performance of the optimized starter is significantly improved with the speed increased by 4.3%， the torque increased by 3.3%， and the power increased by 7.8%.

The high fidelity of digital twin models is crucial for the performance of machine tools in intelligent applications. The electric spindle encounters problems of complex coupling relationships that are difficult to describe and sensitive parameters that are susceptible to change during digital twin modeling， which causes certain errors between the model's response and the real physical system， resulting in limitations in simulation analysis of various stages based on twin models. This article proposes a twin model updating method based on Bayesian inference. Firstly， a twin model of electric spindle based on multi domain unified modeling language is constructed by integrating knowledge from various disciplines； Secondly， the mechanism of parameter changes is anylized during operation， while Bayesian inference method is introduced to construct a twin model update strategy， and solve Bayesian posterior distribution is solved through variational inference by combining actual operation data and prior knowledge； Finally， the effectiveness of the model update method is verified through an example of the machine tool electric spindle. After the update， the simulation error of the model reaches below 5%， effectively improving the fidelity of the twin model of the electric spindle.

To address the issues of high cutting force and elevated cutting temperatures during the milling of 1Cr11Ni2W2MoV heat-resistant stainless steel， optimization of milling parameters was conducted. A 3D milling simulation model was established using AdvantEdge FEM simulation software to investigate the influence of milling parameters on cutting force and cutting temperature. The optimization of milling parameters was achieved through orthogonal experiments. The results indicate that the optimal cutting parameters， based on minimizing cutting force， are a spindle speed of 4000 r/min， a feed per tooth of 0.08 mm/r， and a cutting depth of 0.3 mm. The milling parameter optimization plan based on the lowest cutting temperature is as follows： the spindle speed is 1000 r/min， the feed rate per tooth is 0.12 mm/r， and the cutting depth is 0.3 mm. Adopting a larger spindle speed， smaller feed rate per tooth， and cutting depth is beneficial for reducing cutting force while not affecting production efficiency； Adopting a smaller spindle speed， moderate feed rate per tooth， and smaller cutting depth can lower the cutting temperature.

The decision-making process of CNC machining parameters relies heavily on the engineers' experience， leading to long preparation and unstable machining quality. The oil distributor cap， oil distributor and shell parts are critical components of the hydraulic pump system. Their internal slot features often suffer from low machining efficiency and high costs， which are the key targets for production enterprises' cost reduction and efficiency improvement. Therefore， establishing the relationship between milling process parameters and process indicators is essential for evaluating and optimizing machining parameters， thus improving machining efficiency and ensuring process quality. Due to the complex thermo-mechanical coupling involved in the machining process， the relationship between machining parameters and process indicators is highly nonlinear， making it difficult to model effectively using traditional fitting or data-driven methods. To address this issue， this paper proposes a cutting parameter evaluation model based on a Variational Autoencoder （VAE）， which establishes a predictive model between machining parameters and key physical quantities that reflect cutting characteristics. This model serves as a foundation for optimizing machining parameters in production enterprises. The validation results show that the prediction error of the proposed model is merely 1.2%， fulfilling the requirements for machining parameter optimization.

The simulated moving bed chromatography separation process exhibits characteristics such as strong coupling， nonlinearity and periodicity. The control of this separation process has always been a complex and challenging issue. In this study， Type I fuzzy control strategy and Type II fuzzy control strategy methods are employed to control the purity and recovery rates of components A and B in the simulated moving bed separation process， and comparisons are made with conventional fuzzy control. The research results indicate that both Type I fuzzy control strategy and Type II fuzzy control strategy can achieve the expected control effects for multiple objectives. In comparison， Type II fuzzy control strategy demonstrates higher precision in recovery rate control. Through simulation experiments， the robustness of the two controllers is studied when parameters such as switching time and porosity change， as well as the disturbance resistance of the two controllers under the application of disturbances. Experimental results show that Type II fuzzy control strategy has good control performance， further improving the reliability of the SMB system in unstable environments.

This paper addresses the path tracking control problem of 4WID vehicles under uncertain upper bound disturbance and proposes a sliding mode control method suitable for mismatched uncertain systems. Firstly， considering that disturbances do not necessarily satisfy matching conditions， this paper designs a linear sliding mode surface that is robust to mismatched disturbances based on the Backstepping method and LMI. Secondly， for disturbances that are bounded but with unknown upper bounds， this paper designs a sliding mode control law that adaptively changes the gains of the SMC based on a positive semidefinite barrier function. This method does not require any prior knowledge about the disturbance. Thirdly， a vehicle road tracking model with uncertain sideslip stiffness and road curvature is established， and a lateral motion controller is designed based on this model. Finally， the robustness and stability of the proposed controller are verified by CarSim-Simulink joint simulation. The simulation results show that the proposed controller is robust to uncertain parameters and can effectively eliminate chattering.

The replacement rate of freight train parts is an important data source for freight train remanufacturing enterprises to formulate pre-investment and pre-purchase plans， while the prediction of a more accurate replacement can help enterprises shorten delivery time and reduce inventory. At present， enterprises that fail to fully consider the impact of various factors on the replacement rate during estimation find it difficult to effectively formulate accurate pre-investment and pre-purchase plans， thereby shortening the delivery time and reducing the inventory costs. This paper analyzes the actual business scenarios， and proposes a dynamic model-based method for predicting the replacement rate of freight train parts. This method combines the multi-dimensional factors affecting the replacement rate of freight trains， and uses the support vector machine regression model to construct a dynamic model of the parts replacement rate. By validating the model against historical replacement data of freight train bogie components， the experimental results demonstrate that the model achieves a mean absolute error of less than 15% on the testing set， confirming its effectiveness and applicability in real-world scenarios. This method provides data support for enterprises' pre-investment and pre-purchase decision-making as well as inventory control in the remanufacturing process， which helps enterprises to reduce costs and improve competitiveness.

In this article， a digital twin virtual system of the Rice Processing System is built based on Unreal Engine 5. Aiming at the mutual mapping and integration of physical simulation platform and virtual simulation platform， a digital twin system model of the processing line of preserved rice is constructed.To address the data transmission problem of digital twin five-dimensional system model， a real-time data communication network architecture of digital twin system is constructed， while a new comprehensive network based on Mask RCNN is designed to promote the digital transformation of rice processing to address the issue of detection and segmentation of the fine rice quality with the help of extremely subtle local differences in characteristics. By applying this digital twin system， the detection speed of rice in the production and processing process is greatly improved， the problem of untimely detection data caused by the need for manual inspection in the traditional rice processing industry is solved， and the pain point of the industry where milling accuracy depends on the experience of skilled workers is tackled.

Loading and unloading operations are one of the labor-intensive scenarios in the logistics field. As a crucial issue in automatic loading and unloading， the three-dimensional packing problem is increasingly receiving attention. In response to the shortcomings of traditional 3D packing algorithms that do not pay attention to the order loading and unloading sequence， this paper proposes an efficient and fast two-stage heuristic algorithm for the 3D packing problem under the premise of considering customer order classification and loading and unloading sequence. The algorithm first stacks goods into a "tower" shape and uses dimensionality reduction to transform it into a two-dimensional rectangular filling problem for optimization. It innovatively combines the skyline algorithm with the BL （Bottom Left） algorithm to decode the optimal packing order and position. The experimental results have shown that this algorithm can maximize the space utilization of the packing strategy.

To address the limited transferability issue of traditional deep learning in bearing fault diagnosis under variable operating conditions， an innovative approach that combined Weighted Limited Passage Horizontal Visual Graph （WLPHVG） and Graph Isomorphic Network （GIN） was propossed in this paper. The method transformed raw bearing vibration time-series signals into graph-structured data using WLPHVG and employed the Maximum Mean Discrepancy method to weight the edges connecting graph nodes， reducing the impact of noise on model accuracy. At the same time， adjustments were made to the aggregation layer of the Graph Isomorphic Network to better capture the features of graph data. The graph data was processed then by the final model to obtain the classification results for bearing fault diagnosis. Experimental evaluations on multiple bearing datasets using various models demonstrated that the proposed approach achieved superior accuracy （over 97.27%） under different operating conditions and signal-to-noise ratios compared to that of other benchmark models. The deconstructive experiments validated the effectiveness of the weighting method on the noise resistance of the WLPHVG-GIN model， confirming its ability to leverage internal structural relationships in graph domain data and exhibit strong transferability under diverse conditions such as cross-platform and varying speeds.

To avoid the influence of human subjective factors and a single time-domain and frequency-domain analysis on the detection accuracy of metal equipment in the process of ultrasonic defect detection， this paper proposes to use time-frequency analysis to obtain ultrasonic signal spectrum information， and combine it with visual Transformer model （Ultra-ViT） to realize the recognition and classification of ultrasonic A-scan defect detection signals. Firstly， ultrasonic A-scan is used to obtain the defect signal of the artificial metal defect plate， make the defect signal data set and extract the spectrum characteristics of the defect signal. Using spectral features as training samples， spectral image blocks are extracted， their positions are encoded， and the Ultra-ViT detection model is utilized for training to realize the safety detection of metal equipment. The Experiments show that the recognition accuracy of ultrasonic spectrogram features using Ultra-ViT model is 97.73%. Compared with the classical CNN model， this model can achieve higher detection accuracy with higher detection speed. It shows the superiority of this method in ultrasonic spectrum detection.

A design method for an integrated system for detecting and controlling the thickness uniformity of blown film based on visual servoing is proposed to address issues in the film production process such as uneven discharge leading to quality problems and wastage of resources. The visual detection module is based on machine vision and image semantic segmentation algorithms. By monitoring and recognizing the contour of the formed film using a camera， the image information of the plastic film's outer contour is extracted. Additionally， a midline fitting algorithm is proposed to calculate and analyze the asymmetry of the film， thereby detecting its thickness uniformity. The control module is based on dual-axis interpolation linear motion. With the asymmetry information and position information provided by the detection module， real-time control and feedback are performed on the gap adjustment mechanism of the film mouth on the XY moving platform equipped with two-axis servo motors， ensuring uniform discharge of the film and overall quality. The experimental results demonstrate that the designed system achieves a 97% confidence level in image processing， with a deviation recognition error of 0.485° and an interpolation control error of 0.098 mm， meeting the requirements of the system.