Purpose-In order to solve the problem of inaccurate calculation of index weights,subjectivity and uncertainty of index assessment in the risk assessment process,this study aims to propose a scientific and reasonable c...Purpose-In order to solve the problem of inaccurate calculation of index weights,subjectivity and uncertainty of index assessment in the risk assessment process,this study aims to propose a scientific and reasonable centralized traffic control(CTC)system risk assessment method.Design/methodologylapproach-First,system-theoretic process analysis(STPA)is used to conduct risk analysis on the CTC system and constructs risk assessment indexes based on this analysis.Then,to enhance the accuracy of weight calculation,the fuzzy analytical hierarchy process(FAHP),fuzzy decision-making trial and evaluation laboratory(FDEMATEL)and entropy weight method are employed to calculate the subjective weight,relative weight and objective weight of each index.These three types of weights are combined using game theory to obtain the combined weight for each index.To reduce subjectivity and uncertainty in the assessment process,the backward cloud generator method is utilized to obtain the numerical character(NC)of the cloud model for each index.The NCs of the indexes are then weighted to derive the comprehensive cloud for risk assessment of the CTC system.This cloud model is used to obtain the CTC system's comprehensive risk assessment.The model's similarity measurement method gauges the likeness between the comprehensive risk assessment cloud and the risk standard cloud.Finally,this process yields the risk assessment results for the CTC system.Findings-The cloud model can handle the subjectivity and fuzziness in the risk assessment process well.The cloud model-based risk assessment method was applied to the CTC system risk assessment of a railway group and achieved good results.Originality/value-This study provides a cloud model-based method for risk assessment of CTC systems,which accurately calculates the weight of risk indexes and uses cloud models to reduce uncertainty and subjectivity in the assessment,achieving effective risk assessment of CTC systems.It can provide a reference and theoretical basis for risk management of the CTC system.展开更多
Purpose-The purpose of this paper is to eliminate the fluctuations in train arrival and departure times caused by skewed distributions in interval operation times.These fluctuations arise from random origin and proces...Purpose-The purpose of this paper is to eliminate the fluctuations in train arrival and departure times caused by skewed distributions in interval operation times.These fluctuations arise from random origin and process factors during interval operations and can accumulate over multiple intervals.The aim is to enhance the robustness of high-speed rail station arrival and departure track utilization schemes.Design/methodologylapproach-To achieve this objective,the paper simulates actual train operations,incorporating the fluctuations in interval operation times into the utilization of arrival and departure tracks at the station.The Monte Carlo simulation method is adopted to solve this problem.This approach transforms a nonlinear model,which includes constraints from probability distribution functions and is difficult to solve directly,into a linear programming model that is easier to handle.The method then linearly weights two objectives to optimize the solution.Findings-Through the application of Monte Carlo simulation,the study successfully converts the complex nonlinear model with probability distribution function constraints into a manageable linear programming model.By continuously adjusting the weighting coefficients of the linear objectives,the method is able to optimize the Pareto solution.Notably,this approach does not require extensive scene data to obtain a satisfactory Pareto solution set.Originality/value-The paper contributes to the field by introducing a novel method for optimizing high-speed rail station arrival and departure track utilization in the presence of fluctuations in interval operation times.The use of Monte Carlo simulation to transform the problem into a tractable linear programming model represents a significant advancement.Furthermore,the method's ability to produce satisfactory Pareto solutions without relying on extensive data sets adds to its practical value and applicability in real-world scenarios.展开更多
In this paper,application examples of high-speed electrical machines are presented,and the machine structures are categorized.Key issues of design and control for the high-speed permanent magnet machines are reviewed,...In this paper,application examples of high-speed electrical machines are presented,and the machine structures are categorized.Key issues of design and control for the high-speed permanent magnet machines are reviewed,including bearings selection,rotor dynamics analysis and design,rotor stress analysis and protection,thermal analysis and design,electromagnetic losses analysis and reduction,sensorless control strategies,as well as comparison and selection of sine-wave and square-wave drive modes.Some challenges are also discussed,so that future studies could be focused.展开更多
Iron loss and copper loss are the significant parts of electrical loss of machines,which are the major parts particularly under high frequency condition.High-speed permanent magnet synchronous machines(HS-PMSM)have th...Iron loss and copper loss are the significant parts of electrical loss of machines,which are the major parts particularly under high frequency condition.High-speed permanent magnet synchronous machines(HS-PMSM)have the benefits of high power density,high efficiency and wide speed range.Which causes the calculation for iron loss and copper loss in whole operating range complex.By analyzing the components and influencing factors of iron loss and copper loss in stator,we have deduced the calculation formula of iron loss and copper loss in whole operating range based on the analytical solution and finite element approach(EFA)solution.According to the calculation solution,taking the influence of operating temperature on the iron loss and copper loss into account,we propose a temperature correction factor and establish the calculation method for the iron loss and copper loss with temperature influences.Finally,by the conductor transposition,we restrain the circulating current under high-frequency operating condition.展开更多
With the improvement of coal mining speed and mechanization level in China,traditional tunnel boring methods can no longer meet the actual needs.In order to solve the problems of low efficiency,high labor intensity,sl...With the improvement of coal mining speed and mechanization level in China,traditional tunnel boring methods can no longer meet the actual needs.In order to solve the problems of low efficiency,high labor intensity,slow tunnel boring speed,bad working environment and poor safety in traditional tunnel boring,on the basis of analyzing the development and application of coal roadway tunnel boring equipment at home and abroad,complete equipment for high-speed tunnel boring and bolting machines was developed by using the integrated technology of tunnel boring and bolting.The complete equipment for high-speed tunnel boring and bolting machines has the functions of tunnel boring and bolting synchronization,once-tunneling,negative pressure dust removal,digital guidance,independent cutting feed,digital cutting,safety monitoring and data interaction,which has the advantages of safety in use,reliability and efficiency.展开更多
Purpose–Using the strong motion data ofK-net in Japan,the continuous magnitude prediction method based on support vector machine(SVM)was studied.Design/methodology/approach–In the range of 0.5–10.0 s after the P-wa...Purpose–Using the strong motion data ofK-net in Japan,the continuous magnitude prediction method based on support vector machine(SVM)was studied.Design/methodology/approach–In the range of 0.5–10.0 s after the P-wave arrival,the prediction time window was established at an interval of 0.5 s.12 P-wave characteristic parameters were selected as the model input parameters to construct the earthquake early warning(EEW)magnitude prediction model(SVM-HRM)for high-speed railway based on SVM.Findings–The magnitude prediction results of the SVM-HRM model were compared with the traditional magnitude prediction model and the high-speed railway EEW current norm.Results show that at the 3.0 s time window,themagnitude prediction error of the SVM-HRMmodel is obviously smaller than that of the traditionalτc method and Pd method.The overestimation of small earthquakes is obviously improved,and the construction of the model is not affected by epicenter distance,so it has generalization performance.For earthquake events with themagnitude range of 3–5,the single station realization rate of the SVM-HRMmodel reaches 95%at 0.5 s after the arrival of P-wave,which is better than the first alarm realization rate norm required by“The TestMethod of EEW andMonitoring Systemfor High-Speed Railway.”For earthquake eventswithmagnitudes ranging from3 to 5,5 to 7 and 7 to 8,the single station realization rate of the SVM-HRM model is at 0.5 s,1.5 s and 0.5 s after the P-wave arrival,respectively,which is better than the realization rate norm of multiple stations.Originality/value–At the latest,1.5 s after the P-wave arrival,the SVM-HRM model can issue the first earthquake alarm that meets the norm of magnitude prediction realization rate,which meets the accuracy and continuity requirements of high-speed railway EEW magnitude prediction.展开更多
A numerical control (NC) tool path of digital CAD model is widely generated as a set of short line segments in machining. However, there are three shortcomings in the linear tool path, such as discontinuities of tange...A numerical control (NC) tool path of digital CAD model is widely generated as a set of short line segments in machining. However, there are three shortcomings in the linear tool path, such as discontinuities of tangency and curvature, huge number of line segments, and short lengths of line segments. These disadvantages hinder the development of high speed machining. To smooth the linear tool path and improve machining efficiency of short line segments, this paper presents an optimal feed interpolator based on G^2 continuous Bézier curves for the linear tool path. First, the areas suitable for fitting are screened out based on the geometric characteristics of continuous short segments (CSSs). CSSs in every area are compressed and fitted into a G^2 Continuous Bézier curve by using the least square method. Then a series of cubic Bézier curves are generated. However, the junction between adjacent Bézier curves is only G^0 continuous. By adjusting the control points and inserting Bézier transition curves between adjacent Bézier curves, the G^2 continuous tool path is constructed. The fitting error is estimated by the second-order Taylor formula. Without iteration, the fitting algorithm can be implemented in real-time environment. Second, the optimal feed interpolator considering the comprehensive constraints (such as the chord error constraint, the maximum normal acceleration, servo capacity of each axis, etc.) is proposed. Simulation and experiment are conducted. The results shows that the proposed method can generate smooth path, decrease the amount of segments and reduce machining time for machining of linear tool path. The proposed research provides an effective method for high-speed machining of complex 2-D/3-D profiles described by short line segments.展开更多
High-speed trains(HSTs)have the advantages of comfort,efficiency,and convenience and have gradually become the mainstream means of transportation.As the operating scale of HSTs continues to increase,ensuring their saf...High-speed trains(HSTs)have the advantages of comfort,efficiency,and convenience and have gradually become the mainstream means of transportation.As the operating scale of HSTs continues to increase,ensuring their safety and reliability has become more imperative.As the core component of HST,the reliability of the traction system has a substantially influence on the train.During the long-term operation of HSTs,the core components of the traction system will inevitably experience different degrees of performance degradation and cause various failures,thus threatening the running safety of the train.Therefore,performing fault monitoring and diagnosis on the traction system of the HST is necessary.In recent years,machine learning has been widely used in various pattern recognition tasks and has demonstrated an excellent performance in traction system fault diagnosis.Machine learning has made considerably advancements in traction system fault diagnosis;however,a comprehensive systematic review is still lacking in this field.This paper primarily aims to review the research and application of machine learning in the field of traction system fault diagnosis and assumes the future development blueprint.First,the structure and function of the HST traction system are briefly introduced.Then,the research and application of machine learning in traction system fault diagnosis are comprehensively and systematically reviewed.Finally,the challenges for accurate fault diagnosis under actual operating conditions are revealed,and the future research trends of machine learning in traction systems are discussed.展开更多
Purpose–The purpose of the study is to quickly identify significant heterogeneity of surrounding rock of tunnel face that generally occurs during the construction of large-section rock tunnels of high-speed railways....Purpose–The purpose of the study is to quickly identify significant heterogeneity of surrounding rock of tunnel face that generally occurs during the construction of large-section rock tunnels of high-speed railways.Design/methodology/approach–Relying on the support vector machine(SVM)-based classification model,the nominal classification of blastholes and nominal zoning and classification terms were used to demonstrate the heterogeneity identification method for the surrounding rock of tunnel face,and the identification calculation was carried out for the five test tunnels.Then,the suggestions for local optimization of the support structures of large-section rock tunnels were put forward.Findings–The results show that compared with the two classification models based on neural networks,the SVM-based classification model has a higher classification accuracy when the sample size is small,and the average accuracy can reach 87.9%.After the samples are replaced,the SVM-based classification model can still reach the same accuracy,whose generalization ability is stronger.Originality/value–By applying the identification method described in this paper,the significant heterogeneity characteristics of the surrounding rock in the process of two times of blasting were identified,and the identification results are basically consistent with the actual situation of the tunnel face at the end of blasting,and can provide a basis for local optimization of support parameters.展开更多
In this study,we improved the dispersibility of the stocks in the headbox of an inclined wire machine to produce a distinct paper,and analyzed some factors affecting paper formation in the production of multiply paper...In this study,we improved the dispersibility of the stocks in the headbox of an inclined wire machine to produce a distinct paper,and analyzed some factors affecting paper formation in the production of multiply paper.We used FLUENT6.3 to analyze the flow of the stocks in the headbox and select the structure of the diffusion part required for improving the dispersibility of fibers.Moreover,based on a simulation experiment,the optimal rational angle of the diffusion part(g)was found to be approximately 8°~10°,and it improved the paper formation in the case of usage of two plates.Using the equation for the formation of paper layers in the headbox of an inclined wire machine,we obtained a paper with the given basic weight by controlling the inclined angle of the wire(a),initial height of water(H),and concentration of the stocks.We considered the effect of a and H of the stocks in the headbox on the fiber distribution,and according to the results,a should be set as approximately 20°~30°and H should be maximally high.When producing multi-ply paper by a wire,the line pressure of the couch roll should be maintained at 1.8~2.0 kN/m to avoid the damage to the paper sheets.In addition,we found the optimal structure parameter of the dehydrated roll was as follows:hole ratio of approximately 30%of the dehydrated roll surface area,width of 1.5~2.0 mm,slot pitch of 5~6 mm,slot depth of 2~3 mm,and inclined angle of diffusion part(b)of 5°.展开更多
Applying high-speed machining technology in shop floor has many benefits, such as manufacturing more accurate parts with better surface finishes. The selection of the appropriate machining parameters plays a very impo...Applying high-speed machining technology in shop floor has many benefits, such as manufacturing more accurate parts with better surface finishes. The selection of the appropriate machining parameters plays a very important role in the implementation of high-speed machining technology. The case-based reasoning is used in the developing of high-speed machining database to overcome the shortage of available high-speed cutting parameters in machining data handbooks and shop floors. The high-speed machining database developed in this paper includes two main components: the machining database and the case-base. The machining database stores the cutting parameters, cutting tool data, work pieces and their materials data, and other relative data, while the case-base stores mainly the successfully solved cases that are problems of work pieces and their machining. The case description and case retrieval methods are described to establish the case-based reasoning high-speed machining database. With the case retrieval method, some succeeded cases similar to the new machining problem can be retrieved from the case-base. The solution of the most matched case is evaluated and modified, and then it is regarded as the proposed solution to the new machining problem. After verification, the problem and its solution are packed up into a new case, and are stored in the case-base for future applications.展开更多
The automatic detection and identification of electroencephalogram waves play an important role in the prediction, diagnosis and treatment of epileptic seizures. In this study, a nonlinear dynamics index–approximate ...The automatic detection and identification of electroencephalogram waves play an important role in the prediction, diagnosis and treatment of epileptic seizures. In this study, a nonlinear dynamics index–approximate entropy and a support vector machine that has strong generalization ability were applied to classify electroencephalogram signals at epileptic interictal and ictal periods. Our aim was to verify whether approximate entropy waves can be effectively applied to the automatic real-time detection of epilepsy in the electroencephalogram, and to explore its generalization ability as a classifier trained using a nonlinear dynamics index. Four patients presenting with partial epileptic seizures were included in this study. They were all diagnosed with neocortex localized epilepsy and epileptic foci were clearly observed by electroencephalogram. The electroencephalogram data form the four involved patients were segmented and the characteristic values of each segment, that is, the approximate entropy, were extracted. The support vector machine classifier was constructed with the approximate entropy extracted from one epileptic case, and then electroencephalogram waves of the other three cases were classified, reaching a 93.33% accuracy rate. Our findings suggest that the use of approximate entropy allows the automatic real-time detection of electroencephalogram data in epileptic cases. The combination of approximate entropy and support vector machines shows good generalization ability for the classification of electroencephalogram signals for epilepsy.展开更多
High speed machining and high precision machining are two tendencies of themanufacturing technology worldwide. The motorized spindle is the core component of the machine toolsfor achieving the high speed and high prec...High speed machining and high precision machining are two tendencies of themanufacturing technology worldwide. The motorized spindle is the core component of the machine toolsfor achieving the high speed and high precise machining, which affects the general developmentlevel of the machine tools to a great extent. Progress of the key techniques is reviewed in thispaper, in which the high speed and high precision spindle bearings, the dynamical and thermalcharacteristics of spindles, the design technique of the high frequency motors and the drivers, theanti-electromagnetic damage technique of the motors, and the machining and assembling technique areinvolved. Finally, tha development tendencies of the motorized spindles are presented.展开更多
High-speed machining(HSM) has been studied for several decades and has potential application in various industries, including the automobile and aerospace industries. However,the underlying mechanisms of HSM have not ...High-speed machining(HSM) has been studied for several decades and has potential application in various industries, including the automobile and aerospace industries. However,the underlying mechanisms of HSM have not been formally reviewed thus far. This article focuses on the solid mechanics framework of adiabatic shear band(ASB) onset and material metallurgical microstructural evolutions in HSM. The ASB onset is described using partial differential systems. Several factors in HSM were considered in the systems, and the ASB onset conditions were obtained by solving these systems or applying the perturbation method to the systems. With increasing machining speed, an ASB can be depressed and further eliminated by shock pressure. The damage observed in HSM exhibits common features. Equiaxed fine grains produced by dynamic recrystallization widely cause damage to ductile materials, and amorphization is the common microstructural evolution in brittle materials. Based on previous studies, potential mechanisms for the phenomena in HSM are proposed. These include the thickness variation of the white layer of ductile materials. These proposed mechanisms would be beneficial to deeply understanding the various phenomena in HSM.展开更多
Energy field-assisted machining technology has the potential to overcome the limitations of machining difficult-to-machine metal materials,such as poor machinability,low cutting efficiency,and high energy consumption....Energy field-assisted machining technology has the potential to overcome the limitations of machining difficult-to-machine metal materials,such as poor machinability,low cutting efficiency,and high energy consumption.High-speed dry milling has emerged as a typical green processing technology due to its high processing efficiency and avoidance of cutting fluids.However,the lack of necessary cooling and lubrication in high-speed dry milling makes it difficult to meet the continuous milling requirements for difficult-to-machine metal materials.The introduction of advanced energy-field-assisted green processing technology can improve the machinability of such metallic materials and achieve efficient precision manufacturing,making it a focus of academic and industrial research.In this review,the characteristics and limitations of high-speed dry milling of difficult-to-machine metal materials,including titanium alloys,nickel-based alloys,and high-strength steel,are systematically explored.The laser energy field,ultrasonic energy field,and cryogenic minimum quantity lubrication energy fields are introduced.By analyzing the effects of changing the energy field and cutting parameters on tool wear,chip morphology,cutting force,temperature,and surface quality of the workpiece during milling,the superiority of energy-field-assisted milling of difficult-to-machine metal materials is demonstrated.Finally,the shortcomings and technical challenges of energy-field-assisted milling are summarized in detail,providing feasible ideas for realizing multi-energy field collaborative green machining of difficult-to-machine metal materials in the future.展开更多
基金National Natural Science Foundation of China under Grant 62203468Technological Research and Development Program of China State Railway Group Co.,Ltd.under Grant J2023G007+2 种基金Young Elite Scientist Sponsorship Program by China Association for Science and Technology(CAST)under Grant 2022QNRC001Youth Talent Program Supported by China Railway SocietyResearch Program of Beijing Hua-Tie Information Technology Corporation Limited under Grant 2023HT02.
文摘Purpose-In order to solve the problem of inaccurate calculation of index weights,subjectivity and uncertainty of index assessment in the risk assessment process,this study aims to propose a scientific and reasonable centralized traffic control(CTC)system risk assessment method.Design/methodologylapproach-First,system-theoretic process analysis(STPA)is used to conduct risk analysis on the CTC system and constructs risk assessment indexes based on this analysis.Then,to enhance the accuracy of weight calculation,the fuzzy analytical hierarchy process(FAHP),fuzzy decision-making trial and evaluation laboratory(FDEMATEL)and entropy weight method are employed to calculate the subjective weight,relative weight and objective weight of each index.These three types of weights are combined using game theory to obtain the combined weight for each index.To reduce subjectivity and uncertainty in the assessment process,the backward cloud generator method is utilized to obtain the numerical character(NC)of the cloud model for each index.The NCs of the indexes are then weighted to derive the comprehensive cloud for risk assessment of the CTC system.This cloud model is used to obtain the CTC system's comprehensive risk assessment.The model's similarity measurement method gauges the likeness between the comprehensive risk assessment cloud and the risk standard cloud.Finally,this process yields the risk assessment results for the CTC system.Findings-The cloud model can handle the subjectivity and fuzziness in the risk assessment process well.The cloud model-based risk assessment method was applied to the CTC system risk assessment of a railway group and achieved good results.Originality/value-This study provides a cloud model-based method for risk assessment of CTC systems,which accurately calculates the weight of risk indexes and uses cloud models to reduce uncertainty and subjectivity in the assessment,achieving effective risk assessment of CTC systems.It can provide a reference and theoretical basis for risk management of the CTC system.
文摘Purpose-The purpose of this paper is to eliminate the fluctuations in train arrival and departure times caused by skewed distributions in interval operation times.These fluctuations arise from random origin and process factors during interval operations and can accumulate over multiple intervals.The aim is to enhance the robustness of high-speed rail station arrival and departure track utilization schemes.Design/methodologylapproach-To achieve this objective,the paper simulates actual train operations,incorporating the fluctuations in interval operation times into the utilization of arrival and departure tracks at the station.The Monte Carlo simulation method is adopted to solve this problem.This approach transforms a nonlinear model,which includes constraints from probability distribution functions and is difficult to solve directly,into a linear programming model that is easier to handle.The method then linearly weights two objectives to optimize the solution.Findings-Through the application of Monte Carlo simulation,the study successfully converts the complex nonlinear model with probability distribution function constraints into a manageable linear programming model.By continuously adjusting the weighting coefficients of the linear objectives,the method is able to optimize the Pareto solution.Notably,this approach does not require extensive scene data to obtain a satisfactory Pareto solution set.Originality/value-The paper contributes to the field by introducing a novel method for optimizing high-speed rail station arrival and departure track utilization in the presence of fluctuations in interval operation times.The use of Monte Carlo simulation to transform the problem into a tractable linear programming model represents a significant advancement.Furthermore,the method's ability to produce satisfactory Pareto solutions without relying on extensive data sets adds to its practical value and applicability in real-world scenarios.
基金The authors'team acknowledges the continuous and invaluable support from the Natural Science Foundation of China under the grants of 51577165,51690182,51377140,and 51077116.
文摘In this paper,application examples of high-speed electrical machines are presented,and the machine structures are categorized.Key issues of design and control for the high-speed permanent magnet machines are reviewed,including bearings selection,rotor dynamics analysis and design,rotor stress analysis and protection,thermal analysis and design,electromagnetic losses analysis and reduction,sensorless control strategies,as well as comparison and selection of sine-wave and square-wave drive modes.Some challenges are also discussed,so that future studies could be focused.
基金This work was supported by the National Natural Science Foundation of China(51677144).
文摘Iron loss and copper loss are the significant parts of electrical loss of machines,which are the major parts particularly under high frequency condition.High-speed permanent magnet synchronous machines(HS-PMSM)have the benefits of high power density,high efficiency and wide speed range.Which causes the calculation for iron loss and copper loss in whole operating range complex.By analyzing the components and influencing factors of iron loss and copper loss in stator,we have deduced the calculation formula of iron loss and copper loss in whole operating range based on the analytical solution and finite element approach(EFA)solution.According to the calculation solution,taking the influence of operating temperature on the iron loss and copper loss into account,we propose a temperature correction factor and establish the calculation method for the iron loss and copper loss with temperature influences.Finally,by the conductor transposition,we restrain the circulating current under high-frequency operating condition.
文摘With the improvement of coal mining speed and mechanization level in China,traditional tunnel boring methods can no longer meet the actual needs.In order to solve the problems of low efficiency,high labor intensity,slow tunnel boring speed,bad working environment and poor safety in traditional tunnel boring,on the basis of analyzing the development and application of coal roadway tunnel boring equipment at home and abroad,complete equipment for high-speed tunnel boring and bolting machines was developed by using the integrated technology of tunnel boring and bolting.The complete equipment for high-speed tunnel boring and bolting machines has the functions of tunnel boring and bolting synchronization,once-tunneling,negative pressure dust removal,digital guidance,independent cutting feed,digital cutting,safety monitoring and data interaction,which has the advantages of safety in use,reliability and efficiency.
基金supported by the National Natural Science Foundation of China(U2039209,U1534202,51408564)Natural Science Foundation of Heilongjiang Province(LH2021E119)the National Key Research and Development Program of China(2018YFC1504003).
文摘Purpose–Using the strong motion data ofK-net in Japan,the continuous magnitude prediction method based on support vector machine(SVM)was studied.Design/methodology/approach–In the range of 0.5–10.0 s after the P-wave arrival,the prediction time window was established at an interval of 0.5 s.12 P-wave characteristic parameters were selected as the model input parameters to construct the earthquake early warning(EEW)magnitude prediction model(SVM-HRM)for high-speed railway based on SVM.Findings–The magnitude prediction results of the SVM-HRM model were compared with the traditional magnitude prediction model and the high-speed railway EEW current norm.Results show that at the 3.0 s time window,themagnitude prediction error of the SVM-HRMmodel is obviously smaller than that of the traditionalτc method and Pd method.The overestimation of small earthquakes is obviously improved,and the construction of the model is not affected by epicenter distance,so it has generalization performance.For earthquake events with themagnitude range of 3–5,the single station realization rate of the SVM-HRMmodel reaches 95%at 0.5 s after the arrival of P-wave,which is better than the first alarm realization rate norm required by“The TestMethod of EEW andMonitoring Systemfor High-Speed Railway.”For earthquake eventswithmagnitudes ranging from3 to 5,5 to 7 and 7 to 8,the single station realization rate of the SVM-HRM model is at 0.5 s,1.5 s and 0.5 s after the P-wave arrival,respectively,which is better than the realization rate norm of multiple stations.Originality/value–At the latest,1.5 s after the P-wave arrival,the SVM-HRM model can issue the first earthquake alarm that meets the norm of magnitude prediction realization rate,which meets the accuracy and continuity requirements of high-speed railway EEW magnitude prediction.
基金Supported by National Natural Science Foundation of China(Grant No.50875171)National Hi-tech Research and Development Program of China(863 Program,Grant No.2009AA04Z150)
文摘A numerical control (NC) tool path of digital CAD model is widely generated as a set of short line segments in machining. However, there are three shortcomings in the linear tool path, such as discontinuities of tangency and curvature, huge number of line segments, and short lengths of line segments. These disadvantages hinder the development of high speed machining. To smooth the linear tool path and improve machining efficiency of short line segments, this paper presents an optimal feed interpolator based on G^2 continuous Bézier curves for the linear tool path. First, the areas suitable for fitting are screened out based on the geometric characteristics of continuous short segments (CSSs). CSSs in every area are compressed and fitted into a G^2 Continuous Bézier curve by using the least square method. Then a series of cubic Bézier curves are generated. However, the junction between adjacent Bézier curves is only G^0 continuous. By adjusting the control points and inserting Bézier transition curves between adjacent Bézier curves, the G^2 continuous tool path is constructed. The fitting error is estimated by the second-order Taylor formula. Without iteration, the fitting algorithm can be implemented in real-time environment. Second, the optimal feed interpolator considering the comprehensive constraints (such as the chord error constraint, the maximum normal acceleration, servo capacity of each axis, etc.) is proposed. Simulation and experiment are conducted. The results shows that the proposed method can generate smooth path, decrease the amount of segments and reduce machining time for machining of linear tool path. The proposed research provides an effective method for high-speed machining of complex 2-D/3-D profiles described by short line segments.
基金supported by the National Natural Science Foundation of China(Grant No.71731008)the Beijing Municipal Natural Science Foundation-Rail Transit Joint Research Program(Grant No.L191022)the Zhibo Lucchini Railway Equipment Co.,Ltd.
文摘High-speed trains(HSTs)have the advantages of comfort,efficiency,and convenience and have gradually become the mainstream means of transportation.As the operating scale of HSTs continues to increase,ensuring their safety and reliability has become more imperative.As the core component of HST,the reliability of the traction system has a substantially influence on the train.During the long-term operation of HSTs,the core components of the traction system will inevitably experience different degrees of performance degradation and cause various failures,thus threatening the running safety of the train.Therefore,performing fault monitoring and diagnosis on the traction system of the HST is necessary.In recent years,machine learning has been widely used in various pattern recognition tasks and has demonstrated an excellent performance in traction system fault diagnosis.Machine learning has made considerably advancements in traction system fault diagnosis;however,a comprehensive systematic review is still lacking in this field.This paper primarily aims to review the research and application of machine learning in the field of traction system fault diagnosis and assumes the future development blueprint.First,the structure and function of the HST traction system are briefly introduced.Then,the research and application of machine learning in traction system fault diagnosis are comprehensively and systematically reviewed.Finally,the challenges for accurate fault diagnosis under actual operating conditions are revealed,and the future research trends of machine learning in traction systems are discussed.
基金supported by the Science and Technology Research and Development Program of CHINA RAILWAY(Grant No.K2018G014,K2020G035)the National Natural Science Foundation of China(Grant No.51878567,51878568).
文摘Purpose–The purpose of the study is to quickly identify significant heterogeneity of surrounding rock of tunnel face that generally occurs during the construction of large-section rock tunnels of high-speed railways.Design/methodology/approach–Relying on the support vector machine(SVM)-based classification model,the nominal classification of blastholes and nominal zoning and classification terms were used to demonstrate the heterogeneity identification method for the surrounding rock of tunnel face,and the identification calculation was carried out for the five test tunnels.Then,the suggestions for local optimization of the support structures of large-section rock tunnels were put forward.Findings–The results show that compared with the two classification models based on neural networks,the SVM-based classification model has a higher classification accuracy when the sample size is small,and the average accuracy can reach 87.9%.After the samples are replaced,the SVM-based classification model can still reach the same accuracy,whose generalization ability is stronger.Originality/value–By applying the identification method described in this paper,the significant heterogeneity characteristics of the surrounding rock in the process of two times of blasting were identified,and the identification results are basically consistent with the actual situation of the tunnel face at the end of blasting,and can provide a basis for local optimization of support parameters.
文摘In this study,we improved the dispersibility of the stocks in the headbox of an inclined wire machine to produce a distinct paper,and analyzed some factors affecting paper formation in the production of multiply paper.We used FLUENT6.3 to analyze the flow of the stocks in the headbox and select the structure of the diffusion part required for improving the dispersibility of fibers.Moreover,based on a simulation experiment,the optimal rational angle of the diffusion part(g)was found to be approximately 8°~10°,and it improved the paper formation in the case of usage of two plates.Using the equation for the formation of paper layers in the headbox of an inclined wire machine,we obtained a paper with the given basic weight by controlling the inclined angle of the wire(a),initial height of water(H),and concentration of the stocks.We considered the effect of a and H of the stocks in the headbox on the fiber distribution,and according to the results,a should be set as approximately 20°~30°and H should be maximally high.When producing multi-ply paper by a wire,the line pressure of the couch roll should be maintained at 1.8~2.0 kN/m to avoid the damage to the paper sheets.In addition,we found the optimal structure parameter of the dehydrated roll was as follows:hole ratio of approximately 30%of the dehydrated roll surface area,width of 1.5~2.0 mm,slot pitch of 5~6 mm,slot depth of 2~3 mm,and inclined angle of diffusion part(b)of 5°.
文摘Applying high-speed machining technology in shop floor has many benefits, such as manufacturing more accurate parts with better surface finishes. The selection of the appropriate machining parameters plays a very important role in the implementation of high-speed machining technology. The case-based reasoning is used in the developing of high-speed machining database to overcome the shortage of available high-speed cutting parameters in machining data handbooks and shop floors. The high-speed machining database developed in this paper includes two main components: the machining database and the case-base. The machining database stores the cutting parameters, cutting tool data, work pieces and their materials data, and other relative data, while the case-base stores mainly the successfully solved cases that are problems of work pieces and their machining. The case description and case retrieval methods are described to establish the case-based reasoning high-speed machining database. With the case retrieval method, some succeeded cases similar to the new machining problem can be retrieved from the case-base. The solution of the most matched case is evaluated and modified, and then it is regarded as the proposed solution to the new machining problem. After verification, the problem and its solution are packed up into a new case, and are stored in the case-base for future applications.
基金financially supported by the National Natural Science Foundation of China,No.61263011,81000554Program in Sun Yat-sen University supported by Fundamental Research Funds for the Central Universities,No.11ykpy07+1 种基金Natural Science Foundation of Guangdong Province,No.S2011010005309Innovation Fund of Xinjiang Medical University,No.XJC201209
文摘The automatic detection and identification of electroencephalogram waves play an important role in the prediction, diagnosis and treatment of epileptic seizures. In this study, a nonlinear dynamics index–approximate entropy and a support vector machine that has strong generalization ability were applied to classify electroencephalogram signals at epileptic interictal and ictal periods. Our aim was to verify whether approximate entropy waves can be effectively applied to the automatic real-time detection of epilepsy in the electroencephalogram, and to explore its generalization ability as a classifier trained using a nonlinear dynamics index. Four patients presenting with partial epileptic seizures were included in this study. They were all diagnosed with neocortex localized epilepsy and epileptic foci were clearly observed by electroencephalogram. The electroencephalogram data form the four involved patients were segmented and the characteristic values of each segment, that is, the approximate entropy, were extracted. The support vector machine classifier was constructed with the approximate entropy extracted from one epileptic case, and then electroencephalogram waves of the other three cases were classified, reaching a 93.33% accuracy rate. Our findings suggest that the use of approximate entropy allows the automatic real-time detection of electroencephalogram data in epileptic cases. The combination of approximate entropy and support vector machines shows good generalization ability for the classification of electroencephalogram signals for epilepsy.
文摘High speed machining and high precision machining are two tendencies of themanufacturing technology worldwide. The motorized spindle is the core component of the machine toolsfor achieving the high speed and high precise machining, which affects the general developmentlevel of the machine tools to a great extent. Progress of the key techniques is reviewed in thispaper, in which the high speed and high precision spindle bearings, the dynamical and thermalcharacteristics of spindles, the design technique of the high frequency motors and the drivers, theanti-electromagnetic damage technique of the motors, and the machining and assembling technique areinvolved. Finally, tha development tendencies of the motorized spindles are presented.
基金support of the Shenzhen Science and Technology Innovation Commission under Project Numbers KQTD20190929172505711,JSGG20210420091802007, and JCYJ20210324115413036Guangdong Provincial Department of Science and Technology under Project Number K22333004。
文摘High-speed machining(HSM) has been studied for several decades and has potential application in various industries, including the automobile and aerospace industries. However,the underlying mechanisms of HSM have not been formally reviewed thus far. This article focuses on the solid mechanics framework of adiabatic shear band(ASB) onset and material metallurgical microstructural evolutions in HSM. The ASB onset is described using partial differential systems. Several factors in HSM were considered in the systems, and the ASB onset conditions were obtained by solving these systems or applying the perturbation method to the systems. With increasing machining speed, an ASB can be depressed and further eliminated by shock pressure. The damage observed in HSM exhibits common features. Equiaxed fine grains produced by dynamic recrystallization widely cause damage to ductile materials, and amorphization is the common microstructural evolution in brittle materials. Based on previous studies, potential mechanisms for the phenomena in HSM are proposed. These include the thickness variation of the white layer of ductile materials. These proposed mechanisms would be beneficial to deeply understanding the various phenomena in HSM.
基金supported by the National Key R&D Program of China(Grant No.2020YFB2010500).
文摘Energy field-assisted machining technology has the potential to overcome the limitations of machining difficult-to-machine metal materials,such as poor machinability,low cutting efficiency,and high energy consumption.High-speed dry milling has emerged as a typical green processing technology due to its high processing efficiency and avoidance of cutting fluids.However,the lack of necessary cooling and lubrication in high-speed dry milling makes it difficult to meet the continuous milling requirements for difficult-to-machine metal materials.The introduction of advanced energy-field-assisted green processing technology can improve the machinability of such metallic materials and achieve efficient precision manufacturing,making it a focus of academic and industrial research.In this review,the characteristics and limitations of high-speed dry milling of difficult-to-machine metal materials,including titanium alloys,nickel-based alloys,and high-strength steel,are systematically explored.The laser energy field,ultrasonic energy field,and cryogenic minimum quantity lubrication energy fields are introduced.By analyzing the effects of changing the energy field and cutting parameters on tool wear,chip morphology,cutting force,temperature,and surface quality of the workpiece during milling,the superiority of energy-field-assisted milling of difficult-to-machine metal materials is demonstrated.Finally,the shortcomings and technical challenges of energy-field-assisted milling are summarized in detail,providing feasible ideas for realizing multi-energy field collaborative green machining of difficult-to-machine metal materials in the future.