Due to the narrow and densely-populated space in the cabin of the special vehicle, it is difficult for the air supply system to meet the thermal comfort requirements of all the occupants, and thus it is necessary to optimize the design of the air supply system with reasonable layout of the special vehicle cabin. A simplified three-dimensional model of a type of special vehicle cabin was established. Predicted mean vote (PMV), air age, weighted temperature, and temperature difference between head and foot were selected as the evaluation criteria of the air supply system. Combined with the CFD and multi-objective genetic algorithm, the layout and parameters of the air supply system were simulated and optimized. The simulation results showed that the crew's thermal comfort can be effectively improved by using the top left and right seams and the foot air inlet. With a further analysis of the Pareto optimal solution, we obtained the optimal parameter values of the top air inlet position, air supply angle and return air vent position. The optimized design results meet the requirements of the design indicators, and provide a new idea for the design and optimization of the special vehicle's air supply system.

To solve the sensor arrangement optimization problem for the vibration test of large structures, a tracked vehicle vibration test system integrating sensor arrangement optimization is developed. Firstly, a particle swarm optimization with nonlinear dynamic adjustment of inertial weight collaborative learning factor (PSO-IWLF) is proposed based on the sensor layout optimization theory. Then, different order modal shape values of nodes are obtained through the finite element mode analysis of the tracked vehicle, and the optimization calculation of vibration test acceleration sensor arrangement is carried out according to the modal confidence criterion (MAC). Finally, the tracked vehicle vibration test system (LabMat) is built by MATLAB and LabView. The tracked vehicle vibration test is conducted under typical road spectrum excitation of four types of roads, namely, gobi road, gravel road, paved road and undulating road with vehicle speed of 15 km/h. The calculated results show that the maximum, minimum, mean and root mean square values of vibration acceleration measured by the test are basically consistent with the results from the commercial LabGenius vibration test software, and the error is less than 1%, which verifies the validity of the calculation results of the sensor arrangement optimization and the test system. This study can provide theoretical basis and practical guidance for sensor optimization by the tracked vehicle vibration test and test system design.

Rotating gap oil duct is the main structure of a vehicle’s transmission lubrication system. However, when the transmission system rotates at a high speed, due to the effect of external forces, there is a risk of the insufficient oil supply. Therefore, it is necessary to investigate the influence of various parameters on the flow characteristics of the rotating gap oil duct in the lubrication system of the transmission. In this study, a theoretical model of the flow rate of the rotating gap oil duct is proposed, and the lubrication mechanism and flow characteristics of the rotating gap oil duct are discussed. The effects of the oil temperature, the inlet pressure and the rotating speed on the lubrication oil flow field are analyzed. The lubricating oil flow test is carried out using the rotating gap oil duct test rig for the transmission lubrication system. It is found that with the increasing input shaft speed, the oil flow at the three groups of the radial outlets near the inlet increases slightly, while the oil flow at the gap outlet decreases significantly; with the increasing inlet pressure, the oil flow at each outlet increases, but the flow at the gap outlet increases more significantly. The test results are consistent with the theoretical calculation results, indicating the effectiveness of the theoretical calculation method for the flow rate in the rotating gap oil duct. The proposed method provides a theoretical basis for the accurate oil supply design of the transmission lubrication system.

To solve the problem of predicting the thermal fatigue life of pistons in highly strengthened diesel engines under real conditions, an accelerated thermal fatigue method is proposed to predict the service life of pistons. The critical life method is used to calculate the cycle life of pistons under real working conditions and bench conditions. Piston accelerated thermal fatigue tests are carried out to verify the simulated life data, and the simulation data is in good agreement with the test results. Through damage equivalent analysis, the relationship between piston life under real working conditions and bench life is determined. The correlation between cycle life, loading time and maximum temperature under bench conditions is obtained by nonlinear fitting. Under real working conditions and bench test conditions, the simulation results indicate that the first spot where fatigue failure occurs is the throat area. The failure areas of the two are consistent. When the maximum temperature of piston throat is 408.6 ℃, the service life of piston under real machine calibration condition is 21.2 times that under bench condition. Under the condition of piston bench, when the maximum temperature is the same, the piston life decreases with the decrease of heating time. When the heating time is the same, the piston life decreases with the increase of the maximum temperature. According to the fitted correlation, the activation energy of piston failure is 61.5 kJ/mol.

The uncertainty of human driver behavior brings challenges to the application of advanced driver assistance systems. In order to more accurately predict the lane-changing behavior of a driver, we studied the multi-layer perceptron (MLP) and the support vector machine (SVM), and designed the hybrid algorithm of MLP-SVM to predict the lane-changing behavior of the driver. Based on the vehicle information and the surrounding traffic environment information, the prediction model of driver's lane changing behavior is built. The real traffic dataset is used to verify the proposed model. The results show that compared with the prediction model of driver's lane changing behavior based on support vector machines or multi-layer perceptrons, the hybrid prediction model of driver's lane changing behavior achieves the highest prediction accuracy of 92.6%, and can predict the lane changing behavior earlier with the advanced prediction time up to 4.54 s.

In order to study the mechanical properties and ignition and reaction characteristics of the hafnium/tetrafluoroethylene-hexafluoropropylene-vinylidene fluoride copolymer (Hf/THV) reactive material, specimens with different Hf/THV ratios were prepared by solvent evaporation-hot pressing sintering, and quasi-static compression test and dynamic loading test based on the Split-Hopkinson pressure bar (SHPB) were carried out. The results showed that: Hf/THV broke instantaneously under dynamic loading, which led to the sharp rise of the temperature at the fracture, triggering the formation of hot spots and ignition reaction; the strain softening of THV affected the mechanical properties and failure mode of Hf/THV, and then affected the ignition and reaction of the material; the Hf/THV specimen containing 62wt% Hf showed strong plastic deformation ability due to obvious strain softening, and only after the dynamic loading lasted for a long time did a small part of the specimen fractured, resulting in a long reaction delay time and weak reaction; the Hf/THV specimen containing 88wt% Hf had high strength and strong brittleness, and large-scale fracture occurred in a short time under dynamic loading, resulting in short reaction delay time and violent reaction, but the reduction of oxidant THV led to insufficient reaction.

In order to improve the guidance performance of missiles, on the basis of proportional guidance, Monte Carlo reinforcement learning and Q-learning reinforcement learning are used respectively to design the navigation ratio. The first navigation ratio design method using Monte Carlo reinforcement learning only roughly segments a missile's flight process, whose algorithm is simple with strong engineering usability. The second navigation ratio design method using Q-learning reinforcement learning further subdivides the guidance environment by using flight time, line-of-sight rate, expected encounter time and target characteristics, and adaptively adjusts the navigation ratio of proportional guidance according to the changes of environment and state, so as to obtain the best flight guidance strategy. Based on a certain type of air defense missile, the navigation ratio design is carried out by using the above methods，and the batch trajectories are randomly selected from the whole airspace trajectory library for simulation calculation, which is then compared with the traditional empirical design. The simulation results show that the navigation ratio designed with reinforcement learning can significantly reduce the miss distance of boundary trajectories, indicating that the proposed design method can effectively improve the guidance and interception capabilities of the missile.

To meet the requirement of missiles attacking high maneuvering targets with desired impact angles during terminal guidance, a second-order sliding mode guidance law satisfying impact angle constraint is proposed by combining the super-twisting algorithm with adaptive sliding mode disturbance observer. The disturbance caused by target maneuver leads to the unknown upper bound of system disturbance. Taking target acceleration as system disturbance, an adaptive sliding mode disturbance observer is designed for on-line system disturbance estimation. By adaptively adjusting the observer gain, the defect that the traditional observer depends on the upper bound of disturbance when selecting the gain can be overcome.The improved super-twisting algorithm is designed as the reaching law of the guidance law, enabling the guidance law to make full use of the overload capacity of the missile while reducing the chattering, thus improving the convergence speed of the system, and solving the problem of slow convergence speed when the system state is far from the equilibrium point in the traditional super-twisting algorithm. Based on the Lyapunov stability theory，it is proven that the guidance system can converge in finite time. Finally, the effectiveness of the adaptive sliding mode disturbance observer and the designed guidance law is verified by mathematical simulation. The adaptive sliding mode disturbance observer can accurately track system disturbance, and the designed guidance law can meet the expected terminal angle constraint and has high impact accuracy, with the miss-distance smaller than 0.2 m.

Bionic polarized light navigation is a method of navigation that uses skylight polarization patterns. Due to its strong anti-interference ability and high navigation accuracy, it has attracted extensive attention. However, the field of view is easily blocked during image acquisition, resulting in the loss of image information, which will seriously affect the navigation accuracy. To solve the problem, a polarization navigation algorithm based on local atmosphere image restoration is proposed, and an image inpainting model based on the offsets of similar patches is established. By calculating the nearest neighbor offset histogram of each patch in the image, the offset of each pixel can be obtained, and the image repaired accordingly. Using the restored images, solar azimuth can be determined by detecting the polarization distribution pattern of the atmosphere in the zenith region to achieve the purpose of navigation. The results of simulation and experiments show that the algorithm can adapt to a partially blocked view, remove random occlusion and improve image details. With partial occlusion, the heading angle error is reduced to about 1°, and the accuracy is improved by about 61%.

To accurately predict the separation behaviors of the motive components of an indirect-thrust split nut and the pressure change in the combustion chamber, a mathematical model is established while considering multi-pyrotechnic charges, multi-stage motion equations of the motive components, O-ring friction, and heat loss. Compared with the experimental results, the time history of pressure is predicted with high agreement by the model. If the heat loss and O-ring friction are not considered, the predicted peak pressure and peak velocity of the inner sleeve are overpredicted by 33% and 47%, respectively. The effects of the pyrotechnic charge mass on separation behaviors are investigated using the established model. Increasing the mass of the propellant case and reducing the mass of aluminum potassium perchlorate could effectively improve the reliability of separation and reduce shock.

Distributed cooperative operations of unmanned platform swarms is the trend of future warfare, which calls for rapid response of the intelligent swarm network. However, there is little research on the analysis of wireless swarm ad-hoc networks and distributed multiple-access schemes with intermittent links in complex and dynamic battlefield environments. To this end, a novel distributed coordinated coded multiple access (DC-CMA) scheme is proposed based on non-orthogonal multiple access techniques. The access code pattern of nodes is more efficient than the ones in previous schemes by reducing sparsity and minimizing the waste of resources. Numerical simulation results show that the proposed DC-CMA scheme can reduce the scheme period duration by 10% to 50% compared to the orthogonal multiple access scheme, and improve the resource efficiency by 5% to 30%. An end-to-end transmission delay test is carried out by using the prototype of wireless ad-hoc network nodes to simulate the distributed collaborative task, and the test results show that the DC-CMA scheme can decrease the mean transmission delay by more than 30%, which supports the rapid response of the distributed cooperative intelligent unmanned swarms.

To meet the requirement of satellite independent autonomous navigation in complex electromagnetic environments, a design that integrates Doppler Radar and Synthetic Aperture Radar (SAR) is proposed. Doppler Radar is used to complete high-precision velocity measurements, and the accuracy of the velocity measurements exceeds 0.2%. It can accurately correct the velocity cumulative error of inertial navigation in real time and improve navigation accuracy. SAR imaging technology is used to correct the position cumulative error of inertial navigation through scene matching, and thus to realize high-precision autonomous navigation. At the same time, to reduce the influence of the rigid platform introduced by the integration scheme on the accuracy of SAR imaging, the SAR imaging is compensated in real time by using instantaneous high-precision characteristics of inertial navigation, and the simulation results are given. The simulation results show that the azimuth resolution ratio and the focus depth of the SAR imaging after compensation have greatly improved. The azimuth resolution ratio has been increased from 6.01 m to 2.88 m, and focus depth increased from 1.3 dB to -30 dB, meet the scene matching correction requirements. The conclusion shows that the integrated scheme takes into account both velocity measurement and SAR imaging accuracy, meets the requirements of autonomous navigation operations, and has high effectiveness.

To explore the formation mechanism of ghost nodes in multi-sensor multi-target passive tracking, a method based on set theory is applied to establish a mathematical model for multi-sensor multi-target passive tracking. The method defines bearing measurements as different point sets and targets as the intersections of them. Using the model, the formation conditions of ghost nodes are obtained for three typical scenarios. It is found that the length of sensors' observation baseline, distribution characteristics, and number of sensors are important factors affecting the generation probability of ghost nodes. Three groups of illustrative simulations are conducted to further verify the above factors' practical effects on the likelihood of ghost nodes being traced. The results indicate reducing the length of sensors' observation baseline appropriately, increasing the spatial layout complexity of the sensor network relative to the target group, and using more sensors for observation have a certain positive effect on suppressing the generation of ghost nodes when solving the multi-sensor multi-target passive tracking problem.

Gaussian mixture probability hypothesis density (GM-PHD) filtering based on random finite set (RFS) is an effective method for solving multi-target tracking (MTT) problem. However, the GM-PHD filter in a dense clutter environment may have a poorer tracking performance due to excessive estimation errors. This is mainly because the uncertainty from the multi-target measurement is not fully considered. Every moment, the GM-PHD filter generates a new Gaussian component and a specific measured value. When clutter density is high or the detection probability is low, the accuracy of the estimated value of the target is lower. To solve this problem, this paper proposes a change in the covariance update formula through adjusting the component value with the weight of the Gaussian component considered, and introduces a label to merge the Gaussian components with an adaptive threshold, so as to improve the accuracy of the target number estimation. The proposed algorithm is proven to have a significantly higher target number estimation accuracy and filtering performance than traditional algorithms through tests under tracking scenarios with different clutter and detection probability conditions.

Regarding the immature theory of the second-order magnetic field gradient tensor in magnetic localization, the second-order magnetic gradient tensor contraction method based on magnetic dipole is proposed. The full and partial modulus calculation formulas of the second-order magnetic gradient tensor are proposed. The three-dimensional distribution laws of the full modulus, partial modulus and related parameters of the second-order magnetic gradient tensor are analyzed. The approximate calculation formulas of parameters k_H，k_Hxy and k_Hz are also given. The first-order magnetic gradient tensor, second-order magnetic gradient tensor, and their modulus are compared. The results show that when  is ranged from 0° to 90°, the full modulus C_H and the parameter k_H decrease with the increase of . The maximums values are obtained when  is 0°, and the minimum values obtained when  is 90°. When  is ranged from 0° to 90°, the partial modulus C_Hxy and parameter k_Hxy first increase and then decrease with the increase of . The maximum values are obtained at =35°, and the minimum vaalues obtained at  = 90°. The partial modulus C_Hz and the parameter k_Hz first decrease and then increase as  increases. The maximum values are obtained at = 0°, and the minimum values obtained at =71°. The fitted values of k_H，k_Hxy and k_Hz are highly consistent with those obtained from theoretical inversion. When the distance is small, the second-order magnetic gradient tensor and modulus are more sensitive. When the distance is large, the first-order magnetic gradient tensor and modulus are more sensitive. In practical applications, magnetic localization can be used with the combination of first-order and second-order magnetic gradient tensor and full modulus.

To improve the accuracy and reliability of specific emitter identification, the twin specific emitter identification problem is defined and studied for the first time, and a twin specific emitter identification method based on parallel classification ensemble learning using ant colony feature selection is proposed. The difference between the distribution matrixes of the output results of different classifiers is determined using the Pearson correlation coefficient method. The design model is established for the highest classification accuracy, greatest difference between classifiers, and smallest size of input feature subsets of the parallel classifier based on ant colony feature selection. Meanwhile, combined with the characteristics of the parallel classifier, an ant colony algorithm is designed to solve the model. The weight of each sub-classifier is decided based on the degree of difference and reliability. The more different and reliable the sub-classifier is, the greater the weight will be, The final decision is derived from the weighted sum of classifiers with different weights. To verify the superiority of this method, the parallel classifier based on ant colony feature selection, single classifier, and Adaboost are compared using three groups of data, namely original radio station signals, data with 10 dB white Gaussian noise added, and data with and 5 dB white Gaussian noise added. The experimental results show that the classification accuracy of the proposed model is 88.70%, 76.70% and 64.80% respectively, all outperforming the traditional single classifier, Adaboost algorithm and Random Forest.

Single missile-borne radars are unable to effectively cope with false target jamming in a complex battlefield environment due to a single angle of view, a lack of information, and low anti-jamming efficacy. A method against false-target jamming is thus proposed based on an active/passive missile-borne radar network. This method considers the position error of the missile-borne radar. When the track first begins, angle information is used to identify the measurements of active and passive radar seekers, and some false targets are eliminated. The track initiation of passive missile borne radar is assisted by successfully correlated measurements of the active missile borne radar. Then, the targets are tracked separately by the active and passive missile-borne radars. During track correlation, tracks are associated to identify and eliminate the remaining false targets. Simulation results show that the proposed method can effectively suppress the false-target jamming based on deceptive distance while ensuring high real target discrimination probability.

Trans-media vehicles are an innovative aircraft with long-range rapid striking, repeated entry, and efficient penetration capabilities. However, the turbine working parameters of trans-media vehicles at different stages differ significantly. To realize the aerodynamic design of shared turbines for underwater, in-flight, and takeoff conditions, loss model is integrated to come up with an air-water shared turbine design. Numerical method is used to verify the accuracy of mean-line design method and the feasibility of takeoff conditions. The results show that the relative error between the numerical results and mean-line design results is within 2% under various working conditions. The numerical loss breakdown method is then used to further analyze the turbine losses and reveal that profile loss is the main loss of the air-water shared turbine. The shock waves and separation vortices are found in the passage under underwater conditions. In addition, the partial admission ratio is also very low, resulting in a relatively high loss. During flight, the flow is more uniform and hence the loss is smaller. During takeoff, the turbine works at an off-design point. The output power relies on the underwater nozzle, while air nozzle generates negative power. This study can provide a reference for the optimal design and experiment of air-water shared turbines.

To overcome the problems of low space utilization and fan-shaped blind areas for existing large-opening underwater synchronous scanning laser fuzes, this paper proposes a design with a small opening and five cascaded scanning units. The new scheme realizes the miniaturization of transparent windows, and improves the overall strength and space utilization of the underwater laser fuze. A multi-parameter optimization design method is proposed to obtain the optimal scanning frequency, pulse frequency, and time duration of each scan for laser fuze detection. An echo energy distribution model for various encounter attitudes between the projectile and target has been established. Thus, both the minimum echo energy and its corresponding encounter attitude can be obtained. Computational simulation has also been performed for the typical encounter state of missiles and targets. The simulation results demonstrate that the fuze design proposed in the article can improve the usable space and eliminate the fan-shaped blind area while meeting the requirements for detecting missiles and targets in various encounter attitudes, providing a reference for the design of underwater synchronous scanning laser fuzes.

To improve the navigational stability of high-speed amphibious vehicles and reduce the pitch of the amphibious vehicle when encountering waves, a single vector water jet thruster system that can keep heading and adjust attitude is designed and applied to a model amphibious vehicle, as opposed to a multi-thruster configuration. Based on the Moving Reference Frame method for rotational basins, the RNG k-ε model is used to calculate the output thrust of the vectored waterjet thruster. Combined with the thrust results from the CFD calculations, joint vortex simulations are used to calculate the sailing characteristics of the amphibious vehicle fitted with vector water jet thrusters. The free-running heading-keeping experiment and forced-running pitch-reduction experiment are carried out on the towing pool model vehicle to verify the functions of the thruster. The simulation and experiment results show that the thruster can, to a certain extent, reduce the amphibious vehicle's pitch amplitude when encountering longitudinal waves during navigation, and the standard deviation of the vehicle's pitch is reduced by 38% and 23%, respectively, in the two pitch-reduction experiments. The vector water jet thruster also allows the amphibious vehicle to maintain a stable heading during navigation, and the lateral displacement of the vehicle is less than 0.6 m in the heading-keeping experiment.

To address the difficulties in realizing both functions and high accuracy of an active direction control scheme for self-growing soft robots, an asymmetric fold structure inspired by the abdominal segment structure of marine benthos of the Malacostraca is proposed. The asymmetric fold structure is deployed outside the control chambers to realize asymmetric expansion under inflation pressure. High-precision reversible active direction control of capsules can be realized using this scheme. The manufacturing of a self-growing soft robot prototype is completed based on this scheme. The mathematical model for the relationship between the design parameters such as fold width and fold spacing and performance such as steering radius and angle is proposed, which is then analyzed by theoretical studies and numerical simulation, and further verified by experiments. Results show that the proposed scheme can effectively realize large-angle and high-precision reversible direction control without interfering with the moving ability of the capsule, and a high degree of design freedom can be achieved. The theoretical model can accurately predict the capsule's kinematic performance. The designed self-growing soft robot can be applied to missions such as the investigation of and guidance in narrow non-intervisibility spaces in future urban operations.

Flutter characteristics of high aspect ratio multi-segment folding wings are studied. A beam element considering bending and torsion deformations is used to simulate the two deformation modes of bending and torsion. Combined with the modified aerodynamic strip theory, flutter velocities and flutter frequencies of the high aspect ratio wing at different folding angles are obtained. Considering changes of the folding angle of each segment of the wing, a structural dynamic model including the parameter of folding angles is established based on the finite element theory. Then the dynamic model is reduced and decoupled by modal truncation. According to Theodorsen's unsteady aerodynamic theory, aerodynamic lifts and pitching moments on the bending-torsion beam element are modified in the frequency domain. And an artificial structural damping is introduced based on the V-g method for flutter calculation. The flutter calculations for the multi-segment folding wings under different folding angles reveal that: the natural frequencies of the wing structures show nonlinear growth with the change of folding angles; the variation laws of flutter velocities and flutter frequencies with folding angles are basically the same; the existence of the outer wing folding angle can effectively improve the flutter velocity of the wing; the number of wing segments has no obvious effect on flutter velocities when the outermost wing folding angle is fixed.

The structure and performance of UHMWPE fibers and UHMWPE fiber/SEBS composites before and after thermal oxygen aging were analyzed by FTIR, DSC, DMA, universal testing machine and SEM. The results show that the mechanical properties of the UHMWPE fiber after aging are closely related to the thermo-oxidative aging temperature; the tensile strength of the fiber decreases significantly after high-temperature thermo-oxidative aging, but the crystalline structure of the fiber is not affected; the interface of fiber and resin of UHMWPE fiber/SEBS composites was damaged after thermal-oxidative aging at 70 ℃ and 90 ℃ for 336 h (2 weeks); the flexural strength and flexural modulus of the composites decreased 28% and 49% respectively after thermal-oxidative aging at 90 ℃ for 672 h (4 weeks). However, after 8 weeks of thermo-oxidative aging at 50 ℃, 70 ℃ and 90 ℃, the retention rate of the ballistic limit velocity v₅₀ using 1.1 g standard simulating fragments against the composites was higher than 95%.

To close the research gap on marine titanium alloys at low temperatures and high strain rate rates, Ti6321 titanium alloys with three structures are prepared through heat treatment. The deformation and fracture behaviors of the three alloy samples within the temperature range of -80-25 ℃ and strain rate range of 2 500-3 500 s^-1 are studied. A low-temperature split Hopkinson pressure bar is used for loading. The evolution of the microstructure is observed and analyzed by an optical microscope and scanning electron microscope. The results show that the strength of Ti6321 titanium alloy increases and the plasticity decreases as the temperature declines. The bimodal structure has good strength and plasticity matching at room temperature. With the decrease of temperature, the equiaxed structure has higher strength and better low-temperature plastic deformation capability, and shows good low-temperature dynamic compression properties. The study of the alloy's fracture mechanism shows that the equiaxed and bimodal structures exhibit typical ductile fracture characteristics. The fracture of Widmanstatten structure is an uneven cleavage surface, which has an evident tendency to brittle fracture.

The existing fault diagnosis methods based on deep convolutional neural networks have only considered the extraction of local features while ignoring global information. To address this problem, an attention mechanism that can grasp global information is integrated into the convolution layer. The purpose is to allow both attention mechanism parameters and convolutional layer parameters to participate in network training. An attention augmented convolutional neural network for mechanical fault diagnosis is proposed. First, the high-dimensional feature modulus of the vibration signal of a rolling bearing fault is extracted using empirical mode decomposition, variational mode decomposition, and wavelet packet decomposition. Then, multi-channel samples composed of the feature modulus are input into the attention augmented convolutional neural network for training. The feature modulus are adaptively fused and selected by the network to mine the hidden information. Finally, the classifier Softmax is used to recognize and classify the results. The fault diagnosis of rolling bearings at high rolling speeds is carried out by the trained model. The proposed model is further tested by signals with different signal-to-noise ratios to verify the generalization ability of the network and the effectiveness of fault diagnosis. The experimental results show that the proposed method can accurately and effectively classify the damage degree of different bearing faults in aero-engines.

To solve the problem that the traditional discrete particle swarm optimization (DPSO) algorithm can only find the minimum complete test set for a multivalued attribute system (MVAS), particle swarm optimization for test sequencing (PSO-TS) algorithm is proposed. The diagnosis strategy for MVAS is formulated based on multi-valued D matrix and five-tuple. The implementation process of DPSO is remodeled, and DPSO algorithm is combined with the fault diagnosis strategy of MVAS. Subsequently, a set of calculation rules for self-cognition and the social knowledge are set, and the exchange order is introduced to increase particle diversity. The PSO-TS algorithm is verified using experiments and stochastic simulations. Compared with the MV-Rollout algorithm and MV-IG algorithm, the PSO-TS algorithm can obtain an optimal fault diagnosis strategy with a relatively lower expected test cost, but with a longer running time.

To promote the research and application of electrochemical grinding technology, this study systematically discusses its machining principle and classification. The research status of electrochemical grinding is summarized from the aspects of process performance, material removal method, machining simulation, process optimization, engineering application, and new hybrid electrochemical grinding technology, etc. In view of the problems existing in the current research, the study discusses future research trends of electrochemical grinding from the aspects of machining mechanisms, machining simulation, process parameter optimization, development of special machining equipment, machining modes, and new hybrid electrochemical grinding technologies. This study provides theoretical and technical references for the processing theory and engineering applications of electrochemical grinding, especially for new hybrid electrochemical grinding.