There are many kinds of characteristic curves that characterize involute helical gears. The most well-known are involute and helix with clear geometrical definition. In fact, there are other characteristic curves with outstanding engineering value such as profile of path of contact and contact line on the tooth surface. However, the addition of characteristic lines brings two problems. One is that the complex equations of the characteristic curves are not related to each other and lack mathematical unity. The other is that except for involute and helix, other characteristic curves have no measuring method, which results in a lack of measurability. According to the characteristics of involute gear transmission, the characteristic curves on the tooth surface are mapped to the meshing plane. It is found that each characteristic curves on the tooth surface has its corresponding 2-D straight line in the meshing plane. Based on this, a straight-line model is established to uniformly express various characteristic curves on the tooth surface. On basis of the 3-D deviation measurement data of the gear and the unified model of the characteristic curves, the extraction method of the deviations of various characteristic curves is proposed, which is applied to the measurement practice. By comparing with the profile deviation and helix deviation measured by the general gear measuring instrument, the effectiveness and practicability of the unified model of the characteristic curves and the extraction method of the deviations of the characteristic curves are proved, and the measurability of the complex characteristic curves on the tooth surface is solved. Meanwhile, the unified model of the gear characteristic curves also has important application value in the traceability of gear process error and the prediction of transmission performance.

Based on the rigid body model and small angle assumption, the traditional geometric error model uses a homogeneous transfer matrix (HTM) to establish the volumetric error of a machine tool. Each geometric error will produce a corresponding volumetric error at the corresponding position, but the HTM ignores the influence mechanism of each geometric error on the volumetric error. In order to clearly express the influence mechanism and include more synthesis position variables, a single geometric error model based on the topological sturcture of machine was established. Special emphasis is placed on the relationship between the anglur error and Abbe error. The effect ratio is analysed using the single geometric error model, the large-ratio geometric error is measured and compensated. The results show that the accuracy of the measurement machine is improved by the compensating. The flat accuracy of ϕ150 mm is measured at 344.32 nm, and the concave accuracy of ϕ60 mm is measured at 161.74 nm. The surface maps measured by the coordinate machine are similar to the surface maps measured by interferometer. The establishment process of the proposed error model was simple, which was helpful for understanding the influence mechanism of the angulr error on the Abbe error. The methods presented herin can be applied to design machine tools and improve the accuracy of measurement machine.

The theory of compressed sensing provides a theoretical possibility and a way to realize high-resolution sound source identification and localization with fewer microphones. Therefore, more and more scholars apply the compressed sensing method to solve sound source identification and location problems. Among the existing compressed sensing reconstruction algorithms, orthogonal matching pursuit (OMP) algorithm has the advantages of small sidelobe, high resolution, simple algorithm process, fast calculation speed, and easy hardware implementation, which has the wide application potential. However, the OMP algorithm shows poor positioning performance for low-frequency sound source, and is prone to positioning deviation when the focus plane is densely meshed, which limits the application scope of the algorithm. For this reason, an OMP algorithm based on prior of sound source strengthen is proposed. In this method, prior information of source strengthen is introduced into the atom selection process of OMP, which can better overcome the atomic selection error caused by the high correlation between atoms of sensing matrix when the analysis frequency is low or the focus plane is densely meshed. This algorithm further improves the spatial resolution of the sound source localization and broadens the frequency range to which the algorithm is applicable. In practical applications, it can help us achieve higher accuracy of sound source localization and wide-band sound sources identification.

In order to improve the accuracy of steel surface defect detection in small sample environment, a new method of steel surface defect detection based on the improved auxiliary classifier generative adversarial network (ACGAN) is proposed. Firstly, the residual block is used to optimize the network of ACGAN to improve the feature extraction ability of the model; Secondly, in order to improve the stability of model training, spectral norm normalization is added to the convolution layer of the network to prevent abnormal gradient changes of the model. Then, the loss function of discriminator is optimized based on positive-unlabeled classification to improve the quality of generated samples. At the same time, a gradient penalty is added to the loss function to constrain the gradient of the discriminator in order to alleviate the mode collapse of the Generative Adversarial Network. Finally, the sample expansion is realized through the adversarial optimization training of generator and discriminator. We conducted experiments on steel surface defect datasets to validate the proposed method can accurately and effectively detect steel surface defects in a small sample environment. Compared with the classical support vector machine, ResNet50 and some small sample classification models, the proposed method has higher detection accuracy.

Aeroengine blade is a typical complex surface. In order to realize automatic ultrasonic inspection on blades, an trajectory planning method based on surface point cloud data reconstruction was proposed for the automatic ultrasonic testing. Furthermore, an automatic scanning with seven-axis motion was realized the image this kind complex surface. The blade point cloud data are acquired using linear laser scan and two-axis motion. The surface contour equation is obtained by data fitting method, then acceleration and deceleration trajectory is planning based on the curve equation. An motion decomposition method including the six-axis manipulator and blade rotation axis is developed for each trace point. The actual inspection experiment shows that this path planning algorithm can obtain a clear C-scan image autocratically.

The tubular member is a commonly used engineering member, which will cause heavy losses in the event of an accident. Stress concentration is an important cause of component failure. Magnetic memory testing method is an effective stress concentration testing method. Circumferential scanning magnetic memory detection is performed on cylindrical components, and changes in the action of the earth's magnetic field affect the detection results, which are likely to cause misjudgment of stress concentration areas. The geomagnetic field distribution model on the outer surface of the ferromagnetic cylindrical member is established, and the effect and compensation method of the geomagnetic field on the outer surface of the cylindrical member are explored. The experimental results show that the normal component and amplitude are about twice the geomagnetic field when the ferromagnetic cylindrical member is scanned circumferentially. The normal component of the magnetic signal minus 2 times the geomagnetic field is used as the magnetic memory detection compensation method for cylindrical components, and the correlation coefficient between the compensation and the linear scan is stable above 0.8, which is better than the traditional reverse compensation method. The proposed compensation method for cylindrical components effectively improves the positioning accuracy of stress concentration areas.

There are numerous dissimilar metal welded joints (DMWJs) composed of ferrite heat-resistant steel and austenitic heat-resistant steel/nickel-based alloy in the boiler heating surface pipe system of ultra-supercritical (USC) thermal power units in China. The productive practices demonstrate that most of the DMWJs have early failure after service for 70 000-100 000 hours, and their service life is far less than the design life of 30 years or 200 000 hours. The frequent occurrence of early cracking accidents of DMWJs not only causes great harm to the safe operation of the unit, but also suffers great economic losses and negative social effects to the power generation enterprises. Meanwhile, it also reflects that the current understanding about weldability of dissimilar metals, especially the high temperature service performance of DMWJs, is still insufficient. In order to further reveal the early failure reasons of DMWJs, this work reviewed and summarized the research results of high temperature mechanical properties of 9-12%Cr/Nickel superalloy and 9-12% Cr/Austenitic dissimilar welded joints under high temperature creep and high temperature fatigue around China and other countries in the past two decades. The effects of various factors on the high temperature failure modes and failure characteristics of DMWJs were summarized through the ambient temperature, loading stress, thermodynamic characteristics, welding residual stress, microstructural evolution, et al. The fracture mechanisms of DMWJs under high temperature creep and fatigue conditions were expounded in detail, and it was also introduced a considerable of currently popular physical models about high temperature fracture damage and their development and application. Finally, it was proposed that two effective methods to improve the high temperature mechanical properties of DMWJs, and prospected for the relative researching trend in future.

As the core component of the driving axle, the fatigue life of the axle housing has a great influence on the safety of the drive axle and the whole vehicle. For the welded axle housing, the effect of the welding residual stress should be considered. The fatigue life prediction model of a commercial vehicle axle housing considering welding residual stress via finite element method is established in this paper, the stress and strain response of the axle housing under the bending fatigue test condition are obtained and the strain-life method is applied to predict the fatigue life of the axle housing. The simulation results are compared with the dynamic strain and fatigue life test results of the bending fatigue test, which verifies the accuracy of the fatigue life prediction model in both the fatigue life and the failure location. Compared with the model without considering the welding residual stress, the fatigue life of the axle housing is reduced due to the welding residual stress, and the failure location is different, which indicates the necessity of considering the welding residual stress in the fatigue life prediction. The method presented in this paper could be applied to the fatigue life prediction of structures with welding residual stress, which could provide guidance for structural optimization design.

Based on the classical shell theory and Sanders nonlinear strain-displacement relationship, the geometric nonlinear ordinary differential governing equations of power-law functionally graded material (P-FGM) shallow spherical shells under thermal mechanical loads are derived. One-dimensional heat conduction temperature field along the thickness and normal uniformly distributed load are considered in the derivation. The two-point boundary value problem composed of the governing equations and clamped boundary condition is solved by the shooting method. Some typical buckling equilibrium paths and bistable configurations of FGM shallow spherical shells are obtained. The influence of parameters on the snap-through buckling behavior of FGM shallow spherical shells under thermal mechanical load is analyzed. The results show that when the temperature rises, the upper critical load of the shells increases significantly and the lower critical load does not change obviously. When gradient index increases, the upper and lower critical loads of the shells decrease significantly. When the constituent material modulus increases, the upper and lower critical loads of the shells increase significantly. When bottom circle radius and thickness are given, the upper and lower critical loads of the shells increase significantly with the decrease of the radius of curvature of the shells middle surface. When middle surface curvature radius and thickness of the shells are given, with the increase of the radius of the bottom circle, the lower critical load of the spherical shell decreases significantly, and the upper critical load is almost unchanged.

Lithium-ion batteries (LIBs) are widely used as the main power source for new energy equipment such as electric vehicles and electrical aircraft with their excellent electrochemical energy storage and cycling performance. However, safety issues such as structural failure, internal short circuit and thermal runaway caused by external impact, collision and other loads have severely restricted the development and further application of LIBs. The structural characteristics and mechanical abuse test methods of LIBs is summarized in detail, and multi-field coupling failure mechanism of LIBs from mechanical failure to internal short circuit and thermal runaway under mechanical abuse is expounded. On this basis, the research progress of domestic and foreign scholars in the field of modeling methods for collision safety of lithium-ion batteries in recent years is reviewed systematically. And the research status of modeling methods from aspects of materials constitutive modeling, mechanical modeling and simulation of battery cell, and multi-field coupling modeling methods is summarized. The characteristics, applicability and limitation of various modeling methods are sorted out, and the key issues such as modeling method, simulation accuracy and efficiency are discussed. Finally, the bottleneck problems and further development trend in modeling methods for collision safety of lithium-ion batteries are discussed and prospected. A systematic reference and guidance for the study of crash failure mechanism, modeling and simulation, and safety design of lithium-ion batteries can be provided.

Compared to battery cells, the capacity, life and safety of the battery system will be significantly reduced after forming a group, which is due to the inconsistency problem caused by internal parameters and external environment. Therefore, an equalization management system is needed to guarantee the consistency of the battery, of which the equalization strategy is one of the keys. The research progress of battery equalization strategy at home and abroad is reviewed from four aspects: equalization motivations, equalization objectives, equalization algorithms and equalization strategies evaluation. Firstly, the battery pack consistency influencing factors are analyzed in depth to determine the equalization motivations. Secondly, from the equalization objectives, three research advances in battery pack, circuit and multi-objective fusion are summarized. Again, the equalization algorithms are elaborated according to different algorithm classifications. After that, the evaluation of the equalization strategies are summarized and a new evaluation method is proposed. Finally, the key problems of the current equalization technology that need to be solved are systematically sorted out, and the next research on equalization strategies are prospected.

In the context of the development of intelligence, connectivity, and new energy, the automotive industry combines computer, information communication, artificial intelligence(AI) to achieve integrated development. Based on the new generation of information and communication technology--cloud control system(CCS) of intelligent and connected vehicles(ICVs), the cloud-level automatic driving of new energy vehicles is realized driven by connected data, which provides innovative planning and control ideas for vehicle driving and power systems. Firstly, based on the resource platform of CCS, the latitude, longitude, altitude, and weather of the target road are obtained, and a high definition(HD) path model including slope, curvature, and steering angle is established. Secondly, a deep reinforcement learning(DRL)-based integrated control method for hybrid electric vehicle(HEV) drive by the HD model is proposed. By adopting two DRL algorithms, the speed and steering of the vehicle and the engine and transmission in the powertrain are controlled, and the synchronous learning of four control strategies is realized. Finally, processor-in-the-loop(PIL) tests are performed by using the high-performance edge computing device NVIDIA Jetson AGX Xavier. The results show that under a variable space including 14 states and 4 actions, the DRL -based integrated control strategy realizes the precise control of the speed and steering of the vehicle layer under the high-speed driving cycle of 172 km, and achieves a fuel consumption of 5.53L/100km. Meanwhile, it only consumes 104.14s in the PIL test, which verifies the optimization and real-time performance of the learning-based multi-objective integrated control strategy.

Foreign object damage(FOD) is one of the typical issues leading to fatigue failure of high-speed railway axles. Morphologies of the damage on S38C high-speed railway axles were observed by stereomicroscopy. FODs were simulated by firing tungsten steel spheres or cubes under different velocities and incident angles to the outer surfaces of specimen extracted from S38C axle. Step-loading method was employed to determine the four-point bending fatigue strength. Morphologies of damages were investigated by scanning electron microscopy(SEM), and also the fracture surfaces. Statistics of surface damages on S38C axles show that a large fraction of damages are scratches and the percentage of notch is small. Damage volume of normal impact by spherical projectiles increases with improvement of impact velocity. Material losses and microcracks appear in the crater rim and cracks initiated by adiabatic shear band can be found on the floor. Chipping due to deformation and cutting is the feature of damage by incline impact. The shape of impact damage by tungsten cube is various. Regardless of the impact condition, fatigue strength declines with increase of damage depth and it is feasible to assess impact damage by depth alone. The results provide guidance to the maintenance of S38C railway axles subjected to FODs.

Wheel profile optimization is a good solution to the problem of reduced dynamic performance caused by wheel wear of high-speed trains. A rotary-scaling fine-tuning method(RSFT) is used to generate the new wheel profile; a vehicle dynamics model for the certain high-speed trains is established, and the corresponding optimization objectives and constraints are calculated by the model; the optimal profile is optimized using a radial-based neural network-particle swarm optimization(RBF-PSO) algorithm. By comparing the dynamics and wear performance of the wheel profile before and after the optimization, it can be found that: the wheel profile critical speed after optimization is 424.6 km/h, an increase of 10.2%; the lateral and vertical ride indexes are reduced overall, while the safety indexes during curve passing are improved, and the derailment coefficient, overturning coefficient and lateral axle force are further reduced. The optimized wheel profile has a more evenly distribution of contact points and a reduced equivalent conicity. At the same time, the optimized wheel profile effectively reduces the depth of wheel wear and reduces the root wear of the wheel rim, reducing the maximum depth of wheel wear by 9.8%.

Dual clutch transmission(DCT) has the characteristics of no power interruption and smooth shifting. However, the gear rattle phenomenon of DCT is more obvious after pre-selection. Taking a wet dual clutch transmission as the research object, without considering the shifting process of the transmission, the dynamic models of gear rattle in the 1st gear without pre-selection and preselect the 2nd gear are established respectively. In the model, the drag torque of each loose gear includes the drag torque of oil churning, bearing and synchronizer. In addition, the drag torque generated by the clutch in the non-power flow of the transmission is also considered. Based on considering the elastic contact of tooth surface, the gear contact model takes into account the nonlinear oil film force caused by lubrication effect. Runge-Kutta algorithm is used to solve the model, and the dynamic response of each gear pair is obtained. The feasibility of the dynamic model is verified by bench test, and the calculated results are in good agreement with the experimental results. The dynamic response of transmission gear is studied. The results show that the loose gear of transmission has obvious double-sided rattle phenomenon under certain angular acceleration excitation; pre-selection will not affect the gear pair that has been rattled without preselected gear, but after preselected gear, the non-power flow branches of the transmission will change, and the number of loose gears in the transmission will increase, which makes the rattle phenomenon of the transmission more obvious.

Given the drawback of information loss of high-level features caused by convolution operations when the existing EfficientNet is applied to classify asphaltroad surfaces, a novel dual attention mechanism combined two types of attention modules, named channel attention module and position attention module respectively, is introduced to the existing EfficientNet, and the dual attention network based on EfficientNet (DAEfficientNet) is proposed using sigmoid linear unit (SiLU) activation functions and a cosine learning rate decay technique. First, a dataset including 5, 938 images of five types of asphalt road surfaces under various weather conditions is constructed. The snow image samples of asphalt road surfaces are from the open-source dataset named Canadian adverse driving conditions dataset (CADCD). Second, the proposed model is trained and the image classification results are produced. Finally, the accuracy, precision, recall, F1 score, and specificity of the analyzed models are calculated to compare the classification performance between the proposed model and the others previous convolutional neural network models. The experimental results show that the proposed method outperforms the others completing methods and achieves higher accuracy and stronger robustness in the task of classification of the five types of road surface images.

While the turbocharging system improves automotive acceleration and fuel economy, it also brings serious noise problems. In order to control the pressure relief noise of automotive turbocharging system, the quasi-steady-state response of the pressure relief valve and the transient-state response of the turbocharging system under the pressure relief conditions are analyzed using the computational aeroacoustics method and the dynamic mesh technology. Combining the broadband noise source models and the acoustic analogy method, the pressure relief noise characteristics of the turbocharging system are obtained. The causes of pressure relief noise under continuous transient condition of automotive turbocharger are revealed, and the relationship between pressure relief noise intensity and intake mass flow rate is clarified. The results show that the pressure relief noise with broadband characteristics is mainly caused by vortex shedding of wall, and the sound source intensity decreases with the decrease of mass flow rate. On this basis, a strategy to control pressure relief noise by reducing the intake mass flow rate is proposed, and the structure of the pressure relief valve is redesigned. After the improvement, the area of the high-intensity sound source is significantly reduced, the total sound pressure level is reduced by about 24 dB on average, and the pressure relief noise is effectively controlled. The effect of the pressure relief noise control is also verified by the actual vehicle test of the physical prototype. The research provides a useful reference for the analysis and control of the pressure relief noise of turbocharging systems commonly faced in the automotive industry.

An unsteady aerodynamic characteristics caused by steady crosswind affect the safety and comfort of viaduct operation train. The wind tunnel test of viaduct operation electric multiple units(EMU) under steady cross wind is carried out. The time-history curve of the surface pressure of the car body is tested and recorded to analyze its unsteady aerodynamic characteristics and vibration characteristics. The test results show that: under the same sideslip angle and the same wind speed, the average value of the time course pressure at the same side of car body is not much different, and is basically equal. When the sideslip angle remains unchanged at 90°, the average, maximum and minimum values of the aerodynamic pressure at the measuring point are proportional to the square of the synthetic wind speed; as the wind speed increases, the unsteady pressure fluctuations at the measuring point increase and the fluctuation amplitude is significant increase. When the synthetic wind speed is maintained at 60 m/s, the average, maximum and minimum values of the aerodynamic pressure at the measuring point are proportional to the 1.27 th power of the sideslip angle; as the sideslip angle increases, the peak-to-peak value at the measuring point appears parabolic change law, the amplitude of unsteady fluctuation first decreases and then increases. The main vibration frequency band of viaduct operation EMU coach remains unchanged with the increase of the crosswind wind speed and the same with the increase of the sideslip angle; the vibration frequency of two sides are both in the range of 0-18 Hz, and there are obviously several main vibrations frequency bands are 8-10 Hz, 0-2 Hz, 14-16 Hz and 16-18 Hz respectively.

In order to make full use of fast response and independent control of the electro-hydraulic composite braking system to improve the stability and safety for four-wheel-independent-drive electric vehicles, an anti-lock brake control strategy based on robust integral sliding mode control is proposed. The hierarchical control architecture is adopted, which consists of an upper and a lower controller. The upper controller is in charge of wheel slip ratio control and the lower controller is responsible for the coordination of regenerative braking and hydraulic braking torques. The vehicle dynamics and the composite braking system model are established. The effectiveness of the proposed control strategy is examined and verified under four typical braking conditions based on the Simulink-AMESim-Carsim joint simulation platform. The results show that the proposed control strategy can effectually eliminate the external disturbance and make the wheel slip ratio converge to the expected value without knowing the road adhesion coefficient and the tire longitudinal force. Besides, it also exhibits high robustness to a variety of emergency braking conditions and improves the ride comfort while ensuring braking safety and reliability via coordinating the regenerative braking and the hydraulic braking.

Accurate and reliable battery state of health estimation is the key to ensuring the safe operation of lithium-ion batteries, and provides a reference for failure warning. A general method to estimate the state of health of both battery cells and battery packs is proposed. Firstly, a method for extracting high-quality health indicators of battery cells based on partial charge or discharge data is proposed to ensure the high correlation between health indicators and battery capacity and the online availability of the health indicators. Secondly, a feature generation strategy that considers the capacity attenuation and inconsistency of the battery pack is proposed. The final fusion feature is extracted by using principal component analysis to reduce the dimensionality of the feature matrix. The dual time scale filtering and battery pack equivalent circuit model are combined to broaden the extraction under dynamic discharge conditions. Then, based on the framework of the Gaussian process regression, an improved Gaussian kernel function is proposed considering the overall relationship and local changes of the health indicators and capacity attenuation. Finally, multiple experimental data sets are used to verify the generalization ability of the proposed method under different application conditions. The estimation results show that the proposed method has an estimation error of less than 1.28% for battery cells under constant current discharge conditions, and an estimation error of less than 1.82% for battery cells under dynamic working conditions with changeable environmental temperatures. The verification results for series battery packs show that it can be used in various application scenarios with estimation errors all less than 1.43%. The accuracy of and adaptability in a wide range of application scenarios of battery state of health estimation for battery systems are improved.

With the rapid development and implementation of autonomous driving technology, accurate trajectory tracking for such autonomous vehicles(AVs) has become one of core techniques for fulfilling the AVs motion control in automobile industry and academic research areas. To improve the real-time and accuracy performance of trajectory tracking for the AVs, it is proposed a comprehensive linear time-varying model predictive controller(LTV-MPC) applied to a certain AV. First, a two-degree-of-freedom kinematic model of an AV is constructed in terms of vehicle kinematics principle, Next, based on this 2-DOF kinematic model of AV, a dynamic error model of vehicle's trajectory tracking system is derived using linear time-varying theory, and this model is then linearized by a successive linearization, and the design of this desirable trajectory tracking controller is transformed to a quadratic programming optimized problem in the framework of model predictive control. Finally, the effectiveness of the proposed controller is validated on a self-established test platform under various prescribed reference road trajectories, the results show that this AV with the proposed LTV-MPC can track the prescribed reference road trajectories with high speed and precision, as well as good stability for the AV under various driving conditions.

In order to ensure the trajectory tracking accuracy and driving stability of autonomous vehicles, a model predictive control with variable predictive horizon method was proposed based on on-line identification of vehicle lateral stability state and fuzzy algorithm. Aiming at the online recognition of vehicle stable state, k-means clustering algorithm was used to cluster the parameters of vehicle driving state and obtain the cluster centroid. The real-time safety level of vehicle was obtained by comparing the Euclidean distance between the current vehicle state quantity and different cluster centroids online. At the same time, the lateral offset of the current vehicle track tracking is calculated. With the two as inputs, the variation of prediction time domain is calculated online by fuzzy algorithm and output to MPC controller to realize adaptive adjustment of prediction time domain. Finally, the optimal control sequence of the track tracking of the autonomous vehicle is solved to achieve the goal of high precision trajectory tracking control under the premise of maintaining vehicle stability. The results of CarSim/Simulink co-simulation show that the improved MPC algorithm is superior to the traditional MPC controller in improving the trajectory tracking accuracy and lateral stability of autonomous vehicles.

At present, 3D object detection based on the fusion of lidar and camera has received extensive attention. However, most fusion algorithms are difficult to accurately detect small target objects such as pedestrians and cyclists. Therefore, a feature fusion network based on the self-attention mechanism is proposed, which fully considers the local feature information to achieve accurate 3D object detection. Firstly, to reduce the spatial search range of the point cloud, the Faster-RCNN is improved to form a candidate box. Then, the frustum point cloud was extracted according to the projection relationship between the lidar and the camera. Secondly, a Self-Attention PointNet based on the self-attention mechanism is proposed to segment the original point cloud data within the scope of the frustum. Finally, while using the PointNet and T-Net to predict the 3D bounding box parameters, the regularization term is considered in the loss function to achieve higher convergence accuracy. The KITTI dataset is used for verification and testing. The results show that this method is obviously superior to F-PointNet and the detection accuracy of cars, pedestrians, and cyclists has been greatly improved, and it has higher accuracy than mainstream 3D object detection networks.

Uncertainty factors are usually contained in the mathematical proxy model of complex physical system. In practical engineering problems such as mechanical system reliability optimization design, the key parameters of the model can be calibrated and the model structure can be modified to improve the fidelity of the proxy model. However, for imprecise probabilistic models with mixed uncertainties, the traditional model updating method based on the Euclidean distance is not applicable. To solve this problem, a new model updating method based on the Wasserstein distance measure is proposed, which builds the kernel function based on the Wasserstein distance measure, and uses the geometric properties of Wasserstein distance in P-dimensional parameter space to quantify the differences between different probability distributions. Compared with the existing model updating methods, high-order hyper-parameters of the model can be calibrated to significantly reduce the uncertainty of model structure and parameters. In order to reduce the calculation cost, the approximate Bayesian inference and sliced segmentation technology is further adopted to meet the engineering requirements. The validity of this method for practical engineering problems, such as statics and dynamics, is verified by the constitutive parameter checking problem of forced vibration steel plate and the multidisciplinary uncertainty quantification problem of NASA Langley.

Aiming at the defects of the original AMCL positioning algorithm using only laser information, a semantic map based on laser and vision fusion is proposed for global positioning. The semantic map is fused with a target detection method based on deep learning to extract the corner semantics in the environment; use The established two-dimensional semantic grid map containing corner information, combined with the visual pre-positioning method and the semantic information table around the corner points to improve the efficiency and accuracy of the algorithm's global initial positioning, so that the mobile robot can be used in the situation of a small amount of prior information and motion To achieve positioning more quickly. The method of visual pre-positioning is proposed, the particle weight update method is improved, and the AMCL algorithm is combined with the environment map to perform precise positioning. Finally, a comparative experiment of the built mobile robot in different scenarios verifies the effectiveness of the method.

Considering the influence of uncertainties on protection performance of airbag seat in manned airdrop, a hybrid reliability approach based on probability and probability box models is presented for the protection performance. Firstly, the airbag seat is developed and the numerical model of the "dummy-seat" is established, which is verified by the real equipment airdrop experiment. Then, according to the mixed uncertainty variables in the reliability problem, a reliability analysis model of airbag seat protection characteristics based on probability and probability box hybrid model is constructed. Through equal probability transformation and interval analysis of uncertain variables, the original double-layer nested optimization problem is transformed into a single-layer optimization problem to realize the decoupling of nested optimization problem. Based on this decoupling strategy, the efficiency of solving reliability index could be improved. Finally, the approximate model technology and intergeneration projection genetic algorithm (IP-GA) are adopted to obtained the reliability index. The results demonstrate that the proposed method could effectively evaluate the reliability of protection performance of airbag seat. The method can also be used in the field of other airdrop protections.

Aiming at the problem that the crane luffing process cannot be accurately simulated since the accurate mechanical structure model is not established in the traditional simulation analysis, firstly, based on the analysis of the working principle and simplified mathematical model of the luffing mechanism, the high-precision 3D mechanical structure and hydraulic system simulation analysis models are established respectively based on the software NX and AMESim; Then, the mechanical and hydraulic co-simulation system is built based on mechanical-hydraulic interface in Simcenter 3D(motion analysis module of NX). The key parameter curves such as rodless cavity pressure of the hydraulic cylinder and luffing angle in the luffing rise process are simulated and calculated, and the simulation analysis results based on AMESim in the traditional situation are further calculated; By comparing the results of mechanical and hydraulic co-simulation and simulation analysis based on AMESim with the test results, it can be seen that the distribution curves of key parameters such as rodless cavity pressure and luffing angle analyzed by co-simulation are closer to the test curves, which can greatly improve the simulation analysis accuracy and more accurately simulate the luffing action process of crane.