During the operation of the warp knitting machine, its traverse mechanism may fail due to the out-of-control of traverse control system or the transmission error. In order to realize the effective fault detection for the traverse mechanism of warp knitting machine, a data-driven fault detection method was proposed by combining the combined features of vibration signals and the support vector data description (SVDD) algorithm. Firstly, the vibration signal of the traverse mechanism of warp knitting machine was collected and its time-domain features were extracted. Then, the energy proportion of the vibration signal in each frequency band was obtained by using the wavelet packet decomposition, and the feature vector was constructed by combining the time-domain features. Finally, based on the feature vector of the training samples (only containing normal samples), an independent closed minimum hypersphere was established based on the SVDD algorithm, and the state evaluation for the traverse mechanism of warp knitting machine was realized through comparing the distance from the test sample to the hypersphere center and the hypersphere radius. The fault detection results based on the proposed method were compared with the fault detection results using time-domain features and the SVDD algorithm, as well as using combined features and support vector machine (SVM). The results showed that the fault detection method combining combined features of vibration signals and SVDD algorithm had higher accuracy. The research results can provide a theoretical basis for the accurate fault detection of the traverse mechanism of warp knitting machine, and then provide certain auxiliary decision-making information for the warp knitting machine managers.

The post-weld residual stress treatment process has high nonlinearity and strong parameter coupling, which leads to unstable treatment quality. However, the existing detection method only performs sampling detection, which has the problems of low detection accuracy, longperiod, and can not carry out real-time online detection. Therefore, a new online detection method of post-weld residual stress treatment quality based on the acoustic signal recognition was proposed. In this method, the acoustic signal in the post-weld residual stress treatment process was collected in real time and its features were extracted, and then a post-weld residual stress treatment quality detection model based on the multi-weight neural network was constructed to realize online recognition. The experimental results showed that, compared with traditional detection methods, the proposed method could realize the online detection of post-weld residual stress treatment quality, which could provide reference for parameter optimization and quality control in the post-weld treatment process.

In order to achieve the topological optimization of the volume constraint and the minimum compliance of the continuous structure and solve the numerical instability problems, such as gray-scale element and checkerboard grid caused by classical variable density method, a new topology optimization method was proposed. Firstly, the method of improved solid isotropic material with penalization was used as the material interpolation scheme to establish a structural topology optimization model；secondly, the numerical instability problem was solved by introducing sensitivity filtering method based on the Gaussian weight function and designing a new gray-scale element suppression operator; finally, the optimization model was solved by the optimality criterion method. Through example analysis, it could be seen that the new strategy could improve the topology optimization method. The method had the advantages of faster convergence, acquisition of optimized structures with small compliance and good topological configuration and better suppression of gray-scale element generation. The results provide new ideas for the study of topological optimization of other continuum structures.

driving load is an important control parameter in shield construction, which is directly related to construction safety and efficiency. Through the analysis of influencing factors of driving load, a feature selection and prediction method of driving load based on the analysis of engineering measured data was established. Firstly, the engineering measured data were extreme valuenormalized and preprocessed to reduce the dominant influence caused by dimensional and magnitude differences between different parameters; secondly, through parameter analysis and feature selection based on mutual information, the main influence parameters were selected as input; finally, the prediction model of driving load was established by support vector regression (SVR), and its prediction performance was tested by the actual shield construction engineering case of Tianjin Metro Line 9. The results showed that the established driving load prediction method could select a small number of key features from many influencing parameters contained in the engineering measured data, and realize the reasonable prediction of driving load. The research results can provide a reference for the regulation of shield tunneling parameters, and also provide an idea for the analysis of engineering measured data with many parameters.

Aiming at the problem of lifetime evaluation for highly reliable and long-life semiconductor lasers, a performance degradation assessment method based on the dual-variance stochastic process was proposed. This method not only considered the inherent randomness of the internal failure mechanism of semiconductor lasers, but also took into account measurement random errors caused by human factors, measuring instruments and so on. Firstly, the performance degradation model of semiconductor laser and the maximum likelihood estimation method for its unknown parameters were established. Then, based on the concept of first arrival, the analytical expressions of failure time distribution function and probability density function were given to evaluate the reliability and lifetime of semiconductor lasers. Finally, the applicability and effectiveness of the proposed method were verified by a semiconductor laser lifetime evaluation engineering example. The results showed that compared with the existing performance degradation model, the constructed model had better fitting effect and could improve the accuracy of lifetime evaluation, which could provide strong support for the optimal maintenance decision-making of the semiconductor laser and its whole system.

Aiming at the problem that it is difficult to ensure the accuracy of wing docking during aircraft assembly, which affects the aerodynamic contour and flight stability of the aircraft, a tolerance allocation model based on the node which was measured data of key measurement features was established.Based on measurement aided assembly technology, the key measurement features of aircraft in assembly process were defined.The geometric relationship between key measurement features in wing docking was analyzed, and the measurement uncertainty of on-site measurement equipment was considered. On this basis, a tolerance allocation model with the measured data of key measurement features as nodes was established.The test results showed that the quality and efficiency of aircraft assembly could be significantly improved by using the tolerance allocation model to allocate the tolerances of installation angle, top inverse angle and symmetry of wing, and integrating the allocated tolerance information into the digital assembly system.The research results can provide certain theoretical guidance for the assembly feature definition and tolerance allocation in the docking process of aircraft wings.

In order to realize the real-time wear detection for shovel teeth during the working process of electric shovel, and prevent the mining efficiency of electric shovel from being affected by the shovel tooth wear, a shovel tooth instance segmentation model based on the improved Mask Scoring R-CNN (regional convolutional neural network) was proposed.Firstly, taking the ResNet-101 (residual network) and improved FPN (feature pyramid networks) as the backbone network, the semantic information and detail features of high and low feature layers were extracted and fused, and then the local feature layer was trimmed and normalized by combining with the ROI Align layer, so as to complete target detection and instance segmentation; then, based on the obtained shovel tooth segmentation effect image and binary mask graphic information, the pixel area of shovel tooth in the image after instance segmentation was calculated to judge its wear condition. The results showed that the mean pixel accuracy of the shovel tooth instance segmentation model with ResNet-101 and improved FPN as the backbone network was 90.76% and its mean intersection over union was 83.62% on the test set, which was 1.18% and 1.21% higher than that of the instance segmentation model with ResNet-101 and traditional FPN as the backbone network, respectively. Eight shovel tooth wear detection experiments were carried out at the electric shovel excavation site, and the fluctuation amplitude of detected wear degree of each tooth was less than 2%, and the mean square error was about 0.7, which indicated that the proposed instance segmentation model had good segmentation effect and stability for the shovel tooth, and basically met the requirements of wear detection. The research results can provide new ideas for the intelligent detection of shovel tooth wear state.

In order to reduce the assembly deformation and improve the assembly accuracy of machine tool, an optimization design method of machine tool bed considering assembly deformation was proposed. Firstly, the assembly deformation parts of machine tool and assembly deformation mechanism of the machine tool bed were analyzed, and the factors affecting the assembly deformation of machine tool bed and their influence laws were studied; secondly, based on response surface model and genetic algorithm, a multi-objective optimization method of machine tool bed considering assembly deformation was proposed, and the optimization design process was given; finally, taking the classic machine tool bed as an example, taking the structural parameters of the bed as design variables, and taking the minimum of bed mass, assembly deformation amplitude, maximum static deformation and maximum of first-order natural frequency as the optimization objective, the optimal design of the bed was carried out. The results showed that the number and thickness of stiffener plates along the length and width of the bed had an important influence on the assembly deformation; after optimization, the assembly deformation could be effectively reduced, the assembly accuracy could be improved, and the bed mass smaller, the stiffness was bigger, and the dynamic performance was better. The research results not only provide a reference for the structural analysis and optimization design of foundation large parts of machine tool, but also provide a theoretical basis for the multi-objective optimization of other similar equipment, which has important engineering practice value.

In order to solve the problems of complex control system and difficult machining and assembly of multi-legged robots, a rotary multi-legged bionic robot based on the single-degree-of-freedom Jansen linkage mechanism was designed, and its kinematics analysis and optimization were carried out. Firstly, the degree of freedom of the single bionic mechanical leg of robot was verified by the screw theory, and the kinematics of the bionic mechanical leg was solved by using the complex vector method, so as to obtain the motion trajectory equation of the foot end and the rotation angle of each joint. Then, based on the motion trajectory of the bionic mechanical leg foot end and its influencing factors, the optimization direction was analyzed. And then, a rotary transmission mechanism was proposed and the rotation joint and foot end of the bionic mechanical leg were optimized, at the same time, the gait of the rotary multi-legged bionic robot was analyzed by using the SolidWorks software. Finally, the rotary multi-legged bionic robot prototype was made and its movement ability under normal road conditions was analyzed to verify its feasibility. The results showed that changing the crank length and the horizontal inclination angle of frame could optimize the motion trajectory of the multi-legged bionic robot, which made it more suitable for practical applications; the superposition of the rotary transmission mechanism and multiple bionic mechanical legs could improve the environmental adaptability of the robot. The research results provide an important theoretical basis for the design and engineering application of the follow-up robot system.

Aiming at the complex nonlinear relationship between temperature rise and spindle thermal error caused by multiple heat sources of numerical control (NC) machine tool, a spindle thermal error prediction model (hereinafter referred to as thermal error model) based on chicken swarm optimization (CSO) algorithm and support vector machine (SVM) was proposed. Taking the spindle unit of a precision NC machine tool as the research object, the axial thermal deformation under idle state was measured by five-point measurement method, and the temperatures of four key temperature measuring points of the machine tool were collected using thermocouple sensor. Based on SVM theory, 75% data samples were randomly selected for training, and then the spindle thermal error model was constructed. Among them, CSO algorithm was used to optimize the penalty parameter c and kernel parameter g of SVM model to improve the prediction ability and robustness of the thermal error model. The remaining 25% of the samples were used as the test data set to verify the thermal error model.The spindle thermal error under different working conditions was predicted using CSO-SVM model, and the predicted results were compared with the measured results.The results showed that when the spindle rotate speed was 3 000 r/min, the average prediction accuracy of CSO-SVM model was as high as 97.32%, which was 6.53% and 4.68% higher than that of multiple linear regression model and SVM model based on particle swarm optimization, respectively; when the spindle rotate speed was 2 000 and 4 000 r/min, the average prediction accuracy of CSO-SVM model was 92.53% and 91.82% respectively, indicating that the model had high prediction ability and good robustness. CSO-SVM model has strong practicability and engineering application value.

In order to solve the problem that the electric motor of the radial ring braiding machine was easy to overload or even burn out due to the difference between the electrical characteristics and mechanical characteristics of radial ring braiding machine, the mechatronics system model of the braiding machine was established. The model included a braided ring model and a traction sliding table model. The braided ring model was coupled by the closed transmission of 88 gears and the synchronous drive of 4 permanent magnet synchronous motors, while the traction sliding table model was coupled by drive of permanent magnet synchronous motor and motion of ball screw. In order to solve the uneven load of 4 motors on the braided ring caused by the tooth side clearance, a torque balance control method was proposed, that was, the main motor was controlled by the speed loop and the current loop, and the slave motor was only controlled by current loop; the output of the speed loop of the main motor was taken as the given quantity of the current loop of the slave motor. In order to quickly make the speed of braided ring motor and the speed of sliding table motor meet the cooperative relationship, a decoupling control method for tracking performance and synchronous performance, i.e. cross coupling decoupling control method, was proposed. The tracking error compensation was reduced to less than the amplitude according to a certain proportion, so as to highlight the effect of coordinated error compensation. The field experiment results showed that, the proposed control strategy could make the output torque difference of the 4 motors of the braided ring smaller, and could greatly shorten the time for the speed of the braided ring main motor and the sliding table motor to meet the cooperative requirements in the motor start-up and acceleration and deceleration stages. According to control strategy, the servo control system of the braiding machine was designed. The research results play an important role in improving the weaving quality of fabrics.

The lower limb exoskeleton assisted robot has problems such as whether the human-machine joints match, and whether the active joint design meets the driving force requirements of human joint during motion. In order to solve these problems, based on the designed electro-hydraulic servo driven lower limb exoskeleton assisted robot, by simplifying it into a seven-link structure, the instantaneous dynamic model of swing phase and support phase were constructed by Newton-Euler method combining with the gait balance theory. Then, the angle data, velocity data of human motion under different gait phases and the robot structure parameters were substituted into the Newton-Euler dynamic iteration equations to obtain the theoretical driving torque of each joint of the robot. Finally, the ADAMS (automatic dynamic analysis of mechanical systems) simulation experiment and human-machine cooperative walking aid experiment were carried out, and the correctness and effectiveness of the constructed dynamic iteration equations were verified by comparing the peak driving torque of each joint of the robot. The results showed that using the Newton-Euler method to solve the driving torque of the lower limb exoskeleton assisted robot joint could provide important theoretical support for its structural optimization and control strategy formulation.

In order to reasonably design the large deep-sea autonomous underwater vehicle (AUV), the static configuration problem of its unpowered spiral dive was studied, and the motion characteristics of its unpowered spiral dive were analyzed. Firstly, the dynamic model of large deep-sea AUV was derived based on the Lagrange equation and its direct route test, oblique towing test, cantilever pool test and plane motion mechanism test were numerically simulated by the CFD (computational fluid dynamics) software, and the corresponding hydrodynamic coefficients were fitted by the least square linear regression method; at the same time, the validity of the dynamic model was verified through comparing the route speed of this AUV under the given thrust condition. Then, based on the constructed dynamic model, the six-degree-of-freedom motion simulation model of large deep-sea AUV was established by using the MATLAB/Simulink and S-function, and the relationship between the net negative buoyancy, longitudinal displacement of gravity center, metacentric height and the unpowered spiral diving steady-state parameters was analyzed. Finally, a 1∶10 scaled-down prototype of large deep-sea AUV was designed, and the correctness of dynamic simulation results was verified by the pool test. The results showed that net negative buoyancy was the main power source of large deep-sea AUV, which determined the diving speed and yaw angular speed of the AUV; the greater the net negative buoyancy and the ratio of longitudinal displacement of gravity center to metacentric height, the faster the vertical diving speed of the AUV and the shorter the time to dive to a depth of 6 000 m; due to the large volume of this AUV, its longitudinal inclination angle was mainly determined by the ratio of longitudinal displacement of gravity center to metacentric height, and the influence of ballast mass on the gravity center position and moment of inertia could be almost ignored. The research results can provide a reference for the static configuration of large deep-sea AUV during unpowered spiral diving.

In scientific drilling, the automatic vertical drilling technology can effectively control well deviation. However, the actuator of existing mechanical automatic vertical drilling tool needs a pump with large displacement to provide power. If the displacement is to be reduced, the internal structure of the actuator is required to have a good pressure maintaining effect. In order to solve the above problem, a new actuator with discharge gaps inside was proposed to improve its pressure retention performance, and the streamline distribution in its internal fluid zone was simulated by using the CFD (computational fluid dynamics) method to analyze the influence of discharge gaps on the pressure in the fluid zone, so as to verify the feasibility of using the gap discharge to achieve pressure retention; at the same time, through the sensitivity analysis of parameters such as the width and length of the discharge gap, the influence of different parameters on the pressure retention effect of the new actuator was obtained. The results showed that under the condition of small pump displacement, the new actuator still had good pressure retention effect, and the most obvious factor affecting the pressure retention effect was the discharge gap width; when the discharge gap width was less than 0.2 mm, the overall pressure of fluid zone in this actuator was small, that was, its pressure retention effect was good. It can be seen that the performance optimization of the actuator can be realized by setting up the discharge gap, which is of great significance to further improve the inclination correction ability of the mechanical automatic vertical drilling tool.

In view of the defects such as surface missed welding, buried incomplete penetration and weld penetration during the welding of aluminum sheath of argon arc welded high voltage cable, analternating current field measurement (ACFM) method for weld defects was proposed. Firstly, the ACFM model of weld defect of aluminum sheath of high voltage cable was established by using COMSOL multi-physical field simulation software, and the density distribution characteristics of induced current generated by an exciting coil on a U-shaped core and magnetic field signal characteristics of different types of defect areas of aluminum sheath weld were studied; secondly, an orthogonal receiving coil was designed to obtain the information of length and depth of defects, and the cable aluminum sheath weld specimen with defects and ACFM experimental platform were made; finally, the weld defects of different types of aluminum sheath were detected and the results were analyzed.The experimental results showed that ACFM method could be effectively used to detect the surface missed welding and weld penetration defects of aluminum sheath of high voltage cable with a thickness of 3 mm, and could effectively identify the buried incomplete penetration defects with a buried depth of 2 mm and a length, width and depth of 10, 0.3 and 1 mm respectively.The research results provide an important reference for the identification of weld defects and the evaluation of weld quality of aluminum sheath of high voltage cable.