Winding Function Theory Based Thrust Calculation on Nested-loop Secondary Linear Machine Adapted to Linear Metro

With advantages of strong drive capability,nested-loop secondary linear machine(NLS-LM)has great potentiality in linear metro.For its secondary structure with multiple loops,it is difficult to calculate the electromagnetic thrust of NLS-LM reasonably.Hence,in this paper,one thrust calculation method is proposed considering variable loop inductance and transient loop current.Firstly,to establish the secondary winding function,the modeling domain is confined to a limited range,and the equivalent loop span is employed by analyzing the coupling relationship between primary and secondary.Then,in order to obtain the secondary flux density,the transient secondary current is solved based on the loop impedance and induced voltage.Finally,the electromagnetic thrust can be calculated reasonably by the given primary current sheet and the calculated secondary flux density.Comprehensive simulations and experiments have demonstrated the effectiveness of the proposed method.

An improved YOLOv7 for the state identification of sliding chairs in railway turnout

The sliding chairs are important components that support the switch rail conversion in the railway turnout.Due to the harsh environmental erosion and the attack from the wheel vibration,the failure rate of the sliding chairs accounts for up to 10%of the total failure number in turnout.However,there is little research carried out in the existing literature to diagnose the deterioration states of the sliding chairs.To fill out this gap,by utilizing the images containing the sliding chairs,we propose an improved You Only Look Once version 7(YOLOv7)to identify the state of the sliding chairs.Specifically,to meet the challenge brought by the small inter-class differences among the sliding chair states,we first integrate the Convolutional Block Attention Module(CBAM)into the YOLOv7 backbone to screen the information conducive to state identification.Then,an extra detector for a small object is customized into the YOLOv7 network in order to detect the small-scale sliding chairs in images.Meanwhile,we revise the localization loss in the objective function as the Efficient Intersection over Union(EIoU)to optimize the design of the aspect ratio,which helps the localization of the sliding chairs.Next,to address the issue caused by the varying scales of the sliding chairs,we employ K-means++to optimize the priori selection of the initial anchor boxes.Finally,based on the images collected from real-world turnouts,the proposed method is verified and the results show that our method outperforms the basic YOLOv7 in the state identification of the sliding chairs with 4%improvements in terms of both mean Average Precision@0.5(mAP@0.5)and F1-score.

Finite Control Set Model Predictive Control with Feedback Correction for Power Converters

As an open-loop model predictive control algorithm,finite control set model predictive control(FCS-MPC)scheme in power converter system is based on assumption that responses of optimal control implemented on prediction model agree well with actual system.The influence of model parameter mismatches and environment disturbance on control performance of scheme is neglected.Then,based on feedback correction strategy in traditional model predictive control algorithm,we derive a finite control set model predictive control with feedback correction scheme(FCS-MPCFC)that allows us to adjust prediction model output at current instant by model prediction error at previous instant,and the closed-loop correction of prediction model output is achieved.Simulations comparison analyses on a two-level three-phase inverter with multi-type model parameter mismatches controlled by traditional and improved FCS-MPC scheme are presented.Experiments are carried out on DSP controller platform.

Defects and electrical properties in Al-implanted 4H-SiC after activation annealing

The defects and electrical properties in Al-implanted 4 H-SiC after activation annealing(1600℃-1800℃) are investigated. High temperature annealing can reduce the ion implantation-induced damage effectively, but it may induce extended defects as well, which are investigated by using Rutherford backscattering spectroscopy(RBS/C), secondary ion mass spectroscopy(SIMS), and transmission electron microscopy(TEM) analyses. According to the ratio of the channeled intensity to the random intensity in the region just below the surface scattering peak(Xmin) and RBS/C analysis results, the ion implantation-induced surface damages can be effectively reduced by annealing at temperatures higher than 1700℃,while the defects near the bottom of the ion-implanted layer cannot be completely annealed out by high temperature and long time annealing process, which is also demonstrated by SIMS and TEM analyses. Referring to the defect model and TEM analyses, an optimized annealing condition can be achieved through balancing the generation and elimination of carbon vacancies in the ion implanted layers. Furthermore, the electrical and surface properties are also analyzed, and the hole concentration, mobility, and resistivity are obtained through the Hall effect. The optimized activation annealing conditions of 1800℃/5 min are achieved, under which the lower defects and acceptable electrical properties are obtained.

Improved electrical properties of NO-nitrided SiC/SiO2 interface after electron irradiation

Effective improvement in electrical properties of NO passivated SiC/SiO2 interface after being irradiated by electrons is demonstrated.The density of interface traps after being irradiated by 100-kGy electrons decreases by about one order of magnitude,specifically,from 3×1012 cm-2·eV-1 to 4×1011 cm-2·eV-1 at 0.2 eV below the conduction band of 4H-SiC without any degradation of electric breakdown field.Particularly,the results of x-ray photoelectron spectroscopy measurement show that the C-N bonds are generated near the interface after electron irradiation,indicating that the carbon-related defects are further reduced.

Microstructure and mechanical properties of Al−Mg−Si alloy U-shaped profile

To get a full understanding of hot extrusion,solid solution treatment and aging process on the Al−0.56Mg−0.63Si alloy,the microstructure and mechanical properties of a U-shaped profile were studied through optical microscopy,scanning electrical microscopy,transmission electrical microscopy,hardness,and tensile tests.The coarse equiaxed grains existed near the profile edge as a result of the dynamic recrystallization nucleation and exceeding growth during hot extrusion.The fibrous deformed and sub-structured grains located between the two coarse grain layers,due to the occurrence of work-hardening and dynamic recovery.Perpendicular needle β′′precipitates were distributed inside the grain,and obvious precipitates-free zone appeared after aging treatment.The tensile strength,yield strength and elongation of the aged Al−Mg−Si alloy U-shaped profile were no less than 279.4 MPa,258.6 MPa,and 21.6%,respectively.The fracture morphology showed dimple rupture characteristics.The precipitates and grain boundaries played key role in the strengthening contribution.

Multi-Dimensional Models of SiC Power MOSFET for Accurately Predicting the Characteristics

The paper presents a compact simulation program with integrated circuit emphasis(SPICE)model for a 1200V/19A Silicon Carbide power metallic oxide semiconductor field effect transistor(MOSFET).Based on an equivalent circuit topology,the model completely describes the static and dynamic device characteristics which include the MOSFET channel current,the nonlinear junction capacitance and the switching behavior.Especially the parasitic elements effect is also considered and studied during the modeling,testing and simulation.The model parameters are extracted based on the experimental measurement data.For convenience,the model is implemented by Verilog-A description language and embedded in the simulation software-Advanced Design System(ADS).The validity and accuracy of the model are validated by the double pulse test under inductor load.The Verification result shows that the modeling job is correct and available for prediction of the switching performance under different conditions.

Near-interface oxide traps in 4H–SiC MOS structures fabricated with and without annealing in NO

Near-interface oxide traps （NIOTs） in 4H-SiC metal-oxide-semiconductor （MOS） structures fabricated with and without annealing in NO are systematically investigated in this paper. The properties of NIOTs in SiC MOS structures prepared with and without annealing in NO are studied and compared in detail. Two main categories of the NIOTs, the “slow” and “fast” NIOTs, are revealed and extracted. The densities of the “fast” NIOTs are determined to be 0.761011 cm-2 and 0.471011 cm-2 for the N2 post oxidation annealing （POA） sample and NO POA sample, respectively. The densities of “slow” NIOTs are 0.791011 cm-2 and 9.441011 cm-2 for the NO POA sample and N2 POA sample, respectively. It is found that the NO POA process only can significantly reduce “slow” NIOTs. However, it has a little effect on “fast” NIOTs. The negative and positive constant voltage stresses （CVS） reveal that electrons captured by those “slow” NIOTs and bulk oxide traps （BOTs） are hardly emitted by the constant voltage stress.

Multi-component system maintenance optimization of a rail transit train based on opportunistic correlations

ln order to deal with the problems of insufficient or excessive maintenance in the current maintenance activities of China transit trains,this paper develops a novel multi-component system maintenance optimization approach based on an opportunistic correlation model.Based on the minimal reliability and failure rate change rule of each train component,the novel proposed maintenance optimization benefits from an improved opportunistic maintenance model with system structure correlation,fault correlation and reliability correlation under imperfect maintenance.Then,different maintenance modes can be determined by a proposed mainte-nance factor under the different conditions of components.Specifically,the reliability threshold of each component is also considered to optimize the maintenance cost by the system reliability and operational availability of the train.Furthermore,as the mentioned problem belongs to the NP-Hard optimization problems,a modified particle swarm optimization(PSO)with the improvement of inertia weight is proposed to cope with the optimization problem.Based on a specific case under the practical recorded failure data,the analysis shows that the proposed model and approach can effectively cut the maintenance cost.

Remaining useful life prediction for train bearing based on an ILSTM network with adaptive hyperparameter optimization

Remaining useful life(RUL)prediction for bearing is a significant part of the maintenance of urban rail transit trains.Bearing RUL is closely linked to the reliability and safety of train running,but the current prediction accuracy makes it difficult to meet the re-quirements of high reliability operation.Aiming at the problem,a prediction model based on an improved long short-term memory(ILSTM)network is proposed.Firstly,the variational mode decomposition is used to process the signal,the intrinsic mode function with stronger representation ability is determined according to energy entropy and the degradation feature data is constructed com-bined with the time domain characteristics.Then,to improve learning ability,a rectified linear unit(ReLU)is applied to activate a fully connected layer lying after the long short-term memory(LSTM)network,and the hidden state outputs of the layer are weighted by attention mechanism.The Harris Hawks optimization algorithm is introduced to adaptively set the hyperparameters to improve the performance of the LSTM.Finally,the ILSTM is applied to predict bearing RUL.Through experimental cases,the better perfor-mance in bearing RUL prediction and the effectiveness of each improving measures of the model are validated,and its superiority of hyperparameters setting is demonstrated.

A novel multi-state bogie system reliability evaluation approach by extended d-MC model for high-speed trains

Bogie is a pivotal system and plays a critical part in the safety and reliability management of high-speed rail.However,the available bogie system reliability analysis methods lack the consideration of multi-state characteristics,and the common multi-state reliability analysis methods,being an NP-hard problem,lead to low efficiency.In order to overcome these drawbacks,this paper proposes a novel multi-state rail train bogie system reliability analysis approach based on the extended d-MC model.Three different function interactions within the bogie system are considered to build the multi-state bogie system flow network model.Meanwhile,an extended d-MC model is established to remove unnecessary d-MC candidates and duplicates,which greatly enhances the computing efficiency.The bogie system reliability is calculated,and examples are provided.Numerical experiments are carried out for the different operational conditions of the bogie system and are used to test the practicability of the method proposed in this article;it is found that this method outperforms a newly developed method in solving multi-state reliability problems.

A dimension-reduced neural network-assisted approximate Bayesian computation for inverse heat conduction problems

Due to the flexibility and feasibility of addressing ill-posed problems,the Bayesian method has been widely used in inverse heat conduction problems(IHCPs).However,in the real science and engineering IHCPs,the likelihood function of the Bayesian method is commonly computationally expensive or analytically unavailable.In this study,in order to circumvent this intractable likelihood function,the approximate Bayesian computation(ABC)is expanded to the IHCPs.In ABC,the high dimensional observations in the intractable likelihood function are equalized by their low dimensional summary statistics.Thus,the performance of the ABC depends on the selection of summary statistics.In this study,a machine learning-based ABC(ML-ABC)is proposed to address the complicated selections of the summary statistics.The Auto-Encoder(AE)is a powerful Machine Learning(ML)framework which can compress the observations into very low dimensional summary statistics with little information loss.In addition,in order to accelerate the calculation of the proposed framework,another neural network(NN)is utilized to construct the mapping between the unknowns and the summary statistics.With this mapping,given arbitrary unknowns,the summary statistics can be obtained efficiently without solving the time-consuming forward problem with numerical method.Furthermore,an adaptive nested sampling method(ANSM)is developed to further improve the efficiency of sampling.The performance of the proposed method is demonstrated with two IHCP cases.

Research on pumped two-phase single-sided cold plate of IGBT for rail transit applications

The use of pumped two-phase cooling to improve the thermal management of insulated gate bipolar transistor(IGBT)in rail transportation is a novel cooling technology.An experimental investigation on pumped two-phase cold plate of IGBT used in HXD1C locomotives was conducted at a mass flow rate of 0.1 kg/s–0.29 kg/s and a heat flux of 6.2 W/cm^(2),with R245fa as the working fluid.The experimental results showed that the base temperature nonuniformity can be controlled within 2.2℃ at flow rates of 0.14 kg/s and 0.19 kg/s,which is of great benefit to the reliability of IGBT.Based on well known correlations for saturated flow boiling in tubes,an analytical model was developed and compared with the experimental data.The model could predict the base temperature data within an error band of±3℃,as well as capture the trend of base temperature as a function of vapour quality and mass flow rate.The performance of the pumped two-phase cold plate of IGBT could be further improved with the aid of the developed model.

Dividing and setting strategy of improving calculation efficiency for needle electrode corona discharge with a large-scale space

Needle electrodes are widely used in the research of corona discharge.Aiming at the problem of needle electrode discharge in a large-scale space(tens or even hundreds of centimetres),a hybrid model was proposed in the previous work.However,its indis-pensable multiple iterations result in huge computation and further limit its wide appli-cations.Therefore,a strategy to improve the computing efficiency by setting initial values in different computational domains is put forward in this work.Three types of setting initial values(global constant,partition constant and partition exponent)are simulated and compared in detail.The calculation results show that the calculation efficiency can be increased by 1.4 times simply through setting initial values of charged particles as different constants for the divided subdomains,and further be improved by 4.3 times by setting those in the ionization region as an exponential distribution.The extension of the pro-posed strategy has also been discussed under various voltages,which shows that the results under other voltages can be quickly obtained based on the fitted coefficients under a specific voltage,and the improvement can reach up to 10 times.The accuracy of calculated results has been demonstrated by a needle-plate electrode device with a vertical distance of 25 cm.This research provides an effective strategy for improving the computing efficiency of corona discharge in a large-scale space,and implies the potential in optimizing the analyses of complex electrodes.

Review and prospect of maintenance technology for traction system of high-speed train

In the past few decades,high-speed trains have witnessed tremendous and vigorous development with the appearance of the oil crisis and industrialization,which became a significant trend in the transportation industry the world over.With the increase of high-speed railway mileage,the amount of high-speed train has grown sharply,the service life of the trains has increased gradually and the components of the vehicle traction system have become worn and aged as a result.Therefore,advanced maintenance technology and its application are key factors to reduce maintenance cost,human resource input and ensure safe,stable and reliable operation of trains.This paper summarizes and discusses the development,application mode,maintenance management and maintenance technology of high-speed railways of the major countries in the world,especially discusses the condition-based maintenance and the key technology of the traction electrical system,and offers the prospect of research direction and the development of traction maintenance technology.

Introduction to special issue on high-efficiency and intelligent train traction system

The development of train technology is mainly reflected in the development of the traction system.The train traction system includes several key pieces of equipment,such as the pantograph,traction transformer,traction converter and traction motor.It determines the performance of train start,acceleration,constant speed operation and braking.

Impact of design and process variation on the fabrication of SiC diodes

We have studied the influence of design and process variations on the electrical performance of SiC Schottky diodes. On the design side, two design variations are used in the active cell of the diode（segment design and stripe design）. In addition, there are two more design variations employed for the edge termination layout of the diodes, namely, field limiting ring（FLR） and junction termination extension（JTE）. On the process side, some diodes have gone through an N;O annealing step. The segment design resulted in a lower forward voltage drop（VF） in the diodes and the FLR design turned out to be a better choice for blocking voltages, in the reverse bias.Also, N;O annealing has shown a detrimental effect on the diodes’ blocking performance, which have JTE as their termination design. It degrades the blocking capability of the diodes significantly.