High-speed Electric Multiple Unit Research Articles

Investigation on influencing factors of wheel polygonization of a plateau high-speed EMU train

PurposeThis study aims to investigate the cause of high-order wheel polygonization in a plateau high-speed electric multiple unit (EMU) train.Design/methodology/approachA series of field tests were conducted to measure the vibration accelerations of the axle box and bogie when the wheels of the EMU train passed through tracks with normal rail roughness after re-profiling. Additionally, the dynamic characteristics of the track, wheelset and bogie were also measured. These measurements provided insights into the mechanisms that lead to wheel polygonization.FindingsThe results of the field tests indicate that wheel polygonal wear in the EMU train primarily exhibits 14–16 and 25–27 harmonic orders. The passing frequencies of wheel polygonization were approximately 283–323 Hz and 505–545 Hz, which closely match the dominated frequencies of axle box and bogie vibrations. These findings suggest that the fixed-frequency vibrations originate from the natural modes of the wheelset and bogie, which can be excited by wheel/rail irregularities.Originality/valueThe study provides novel insights into the mechanisms of high-order wheel polygonization in plateau high-speed EMU trains. Futher, the results indicate that operating the EMU train on mixed lines at variable speeds could potentially mitigate high-order polygonal wear, providing practical value for improving the safety, performance and maintenance efficiency of high-speed EMU trains.

Establishment and Analysis of Load Spectrum for Bogie Frame of High-Speed Train at 400 km/h Speed Level

The bogie frame, as one of the most critical load-bearing structures of the Electric Multiple Unit (EMU), is responsible for bearing and transmitting various loads from the car body, wheelsets, and its own installation components. With the increasing operating speed of high-speed EMUs, especially when the design and operational speeds exceed 400 km/h, the applicability of current international standards is uncertain. The load spectrum serves as the foundation for structural reliability design and fatigue evaluation. In this paper, the measured loads of the bogie frame of a CR400AF high-speed train on the Beijing–Shanghai high-speed railway are obtained, and the time-domain characteristic of the measured loads is analyzed under different operating conditions. Then, through the Weibull distribution of three parameters, the Weibull parameters at the 450 km/h speed level are fitted, and the maximum load and cumulative frequency under the speed level are derived. Finally, the load spectrum of the bogie frame at the 450 km/h speed level is established, which provides a more realistic load condition for accurately evaluating the fatigue strength of bogie frames at higher speed levels.

Accurate Identification of Partial Discharge Signals in Cable Terminations of High-Speed Electric Multiple Unit Using Wavelet Transform and Deep Belief Network

Cable termination serves as a crucial carrier for high-speed train power transmission and a weak part of the cable insulation system. Partial discharge detection plays a significant role in evaluating insulation status. However, field testing signals are often contaminated by external corona interference, which affects detection accuracy. This paper proposes a classification model based on wavelet transform (WT) and deep belief network (DBN) to accurately and rapidly identify corona discharge in the partial discharge signals of vehicle-mounted cable terminals. The method utilizes wavelet transform for noise reduction, employing the sigmoid activation function and analyzing the impact of WT on DBN classification performance. Research indicates that this method can achieve an accuracy of over 89% even with limited training samples. Finally, the reliability of the proposed classification model is verified using measured mixed signals.

Simulation Analysis and Experimental Study on the Fluid–Solid–Thermal Coupling of Traction Motor Bearings

The traction motor is a crucial component of high-speed electric multiple units, and its operational reliability is directly impacted by the temperature increase in the bearings. To accurately predict and simulate the temperature change process of traction motor bearings during operation, a fluid–solid–thermal simulation analysis model of grease-lubricated deep groove ball bearings was constructed. This model aimed to simulate the temperature rise of the bearing and the grease flow process, which was validated through experiments. The results from the simulation analysis and tests indicate that the temperature in the contact zone between the bearing rolling element and the raceway, as well as the ring temperature, initially increases to a peak and then gradually decreases, eventually stabilizing once the bearing’s heat generation power and heat transfer power reach equilibrium. Furthermore, the established fluid–solid–thermal coupling simulation analysis model can accurately predict the amount of grease required for effective lubrication in the bearing cavity, which stabilizes along with the bearing temperature. The findings of this research can serve as a theoretical foundation and technical support for monitoring the health status of high-speed EMU traction motor bearings.

Convolutional Neural Network-Based Pattern Recognition of Partial Discharge in High-Speed Electric-Multiple-Unit Cable Termination.

Partial discharge detection is considered a crucial technique for evaluating insulation performance and identifying defect types in cable terminals of high-speed electric multiple units (EMUs). In this study, terminal samples exhibiting four typical defects were prepared from high-speed EMUs. A cable discharge testing system, utilizing high-frequency current sensing, was developed to collect discharge signals, and datasets corresponding to these defects were established. This study proposes the use of the convolutional neural network (CNN) for the classification of discharge signals associated with specific defects, comparing this method with two existing neural network (NN)-based classification models that employ the back-propagation NN and the radial basis function NN, respectively. The comparative results demonstrate that the CNN-based model excels in accurately identifying signals from various defect types in the cable terminals of high-speed EMUs, surpassing the two existing NN-based classification models.

Towards multi-scene learning: A novel cross-domain adaptation model based on sparse filter for traction motor bearing fault diagnosis in high-speed EMU

Fault diagnosis of traction motor bearing is of great significance to improve the reliability and safety of high-speed electric multiple units (EMU). While the fault diagnosis method based on cross-domain adaptation has been successful in scenarios involving speed or load fluctuations, existing methods ignore the independence and diversity of features, resulting in unsatisfactory diagnostic results under multi-scene learning, thereby reducing the generalization ability. Moreover, the development of complex models is time-consuming, and their computational efficiency is low. To address these issues, this study proposes a novel cross-domain adaptation model based on sparse filtering (SFCDA), which consists of only two fully connected (FC) layers. Firstly, pre-training is conducted to utilize the soft reconstruction penalties to constrain the weights of sparse filtering and improve the independence of features. The weights of unsupervised training are used to initialize the parameters of the first FC layer of the SFCDA model. Secondly, a multiple sparse regularization (MSR) algorithm is proposed and used to constrain the SFCDA. Then, fine-tuning is conducted, in which the structural alignment function is used to measure the distribution distance between the source and target domain data. Minimizing the kernel norm can improve the diversity of features and enhance the robustness. Finally, the effectiveness of SFCDA in multi-scene learning is proved theoretically. It is validated in three fault diagnosis scenes in four different bearing datasets. The results show that the suggested approach is more straightforward and has a better fault diagnosis effect than the state-of-the-art domain adaptive approaches.

A method for modifying octagonal Goodman–Smith fatigue limit diagram

PurposeIn order to extend the application of the original octagonal Goodman–Smith fatigue limit diagram, which is commonly used for the evaluation of structure fatigue stress in engineering, a modification of it is proposed for the structure made of S355 steel (commonly used in high-speed electric multiple units (EMUs) bogie frame).Design/methodology/approachThe modification is made based on Deutscher Verband für Schweißen und verwandte Verfahren e. V. (DVS) 1612 standard and the γ-P-S-N curve, with consideration of the fatigue evaluation requirements of different survival rates and confidence levels. The verification of the modification is performed for three welded joints and for the comparison with the experimental data.FindingsThe results indicate that the design survival rate, the design safety margin and the fatigue stress evaluation of welded joint types are all improved by using the modified diagram.Originality/valueThere are relatively few studies on modifying octagonal Goodman–Smith fatigue limit diagram. In this paper, a modified diagram is proposed and applied in order to ensure the safety and durability of key welded structures of rail vehicles.

Classification of Partial Discharge in Vehicle-Mounted Cable Termination of High-Speed Electric Multiple Unit: A Machine Learning-Based Approach

This paper presents a machine learning-based approach to identify and separate partial discharge (PD) and two typical pulse interference (PI) signals in the vehicle-mounted cable terminations of high-speed electric multiple units (EMUs). First, a test platform was established to capture PD and two typical PI signals in these terminations. The acquired signals were then processed using the square envelope method to extract feature parameters, such as the rise time proportion, the left–right symmetry, and the upper–lower symmetry. PD signal classification was carried out on these signals, utilizing waveform parameters derived from a hierarchical clustering algorithm. The results validate that the extracted feature components effectively classify and separate PD and two typical PI signals in the vehicle-mounted cable terminations of high-speed EMUs.

Fine Tuning of On-Board Traction Converters for High-Speed Electric Multiple Units at Depot

Fine Tuning of On-Board Traction Converters for High-Speed Electric Multiple Units at Depot

Study on Oil-Gas Separation Characteristics of Gearbox Sealing System of High-Speed Electric Multiple Unit

Based on the discrete phase model (DPM) and droplet-wall collision model, this paper analyzes the oil-gas separation characteristic and sealing performance of a gearbox sealing system. The results show that the airflow drag force and oil droplet inertia force affect the oil-gas separation efficiency of the sealing system. Furthermore, larger oil droplet diameter leads to a higher separation efficiency and lower outlet back pressure reduces the separation efficiency of smaller diameter oil droplet and increases the separation efficiency of larger diameter droplet. For oil droplet with an intermediate diameter, the separation efficiency first decreases and then increases with the reduction in back pressure.

A novel roundness error evaluation method for high-speed EMU train axles

This article proposes a novel roundness error evaluation method for high-speed electric multiple units (EMU) train axles to evaluate the roundness error of the minimum zone circle. This method utilizes the adaptive ability of Bayesian linear regression to the data to solve the initial center of the circle, avoiding the multiple search process, which not only improves the data utilization and simplifies the solving steps but also has a strong noise immunity. The method first establishes a Hough space model, maps the collected data points to the Hough space, and forms different conical surfaces; then, by Bayesian linear regression, the probability density function is set with the distance from the space point to the conical surface as a parameter, and through continuous iteration, the point with the highest probability value is the more accurate initial center of the circle. Finally, the final optimal center of the circle is obtained by the principle of minimum inclusion region. We conducted experimental studies on axles of different diameters and compared them with other methods. The results prove the practicality and effectiveness of the technique.

Noise Prediction Study of Traction Arc Tooth Cylindrical Gears for New Generation High-Speed Electric Multiple Units

As the speed of the new generation of high-speed electric multiple units (EMU) increases, the requirements for vibration and noise reduction in traction gear trains are becoming higher and higher. Although most researchers have focused on the vibration mechanics analysis of gears, the actual noise has the most direct impact on passenger experience and safety. To address this problem, a new type of curved cylindrical gear is proposed to analyze the dynamic characteristics of the gear pair and predict its radiated noise based on the acoustic-vibration coupling theory using the finite element-boundary element method. Parametric modeling of the gear pair using CREO and assembly motion analysis were performed. ANSYS was used to analyze the stress distribution, inherent frequency, and inherent vibration pattern of the gear pair, and harmonic response analysis was performed using the modal superposition method to solve the displacement frequency response curve and vibration characteristics. ACTRAN was used to construct the free-field model, create acoustic excitation based on the acoustic-vibration coupling equation, set the field points, and predict radiated noise. The research results show that the noise is mainly concentrated in the tooth meshing area, and the root mean square RMS range of its sound pressure level value is 91–100 dB. Its dynamic characteristics and noise values are in line with the traction requirements of high-speed EMU, providing a new idea for improving the noise prediction of traction gears for new generation high-speed EMU, which in turn strongly support the noise control of high-speed EMU stock and thus improve the passenger experience and driving environment.

Risk Assessment Analysis of Multiple Failure Modes Using the Fuzzy Rough FMECA Method: A Case of FACDG

With the increasing operating mileage and ownership of high-speed electric multiple units (EMU), a reasonable operation and maintenance strategy is the key to ensure their safe and reliable operation. As a key component of recombined EMU, creating a reasonable and effective risk assessment method for the fully automatic coupler draft gear (FACDG) is the first task. Therefore, based on fuzzy rough number theory, combined with the analytic hierarchy process (AHP), entropy weight method (EWM), technique for order performance by similarity to ideal solution (TOPSIS) and grey relational analysis (GRA), a risk priority indicator of comprehensive nearness degree is developed. Furthermore, a novel multi-criteria decision making (MCDM) failure modes, effects and criticality analysis (FMECA) assessment method is proposed. The effectiveness and rationality of the risk assessment method proposed are verified by the analysis of data and failure modes of a certain FACDG at fourth-level engineering maintenance.

Research on Rolling Contact Fatigue Failure of the Bearing Used in High-Speed Electric Multiple Units’ Axle Box Based on a Damage-Coupled Elastic–Plastic Constitutive Model

The axle box bearing is a crucial component of high-speed electric multiple units (EMU) and is exposed to harsh working conditions, making it susceptible to subsurface-induced rolling contact fatigue (RCF) under long-term alternating stress. The objective of this paper is to develop a damage-coupled elastic–plastic constitutive model that can accurately predict the RCF life of EMU axle box bearings made from AISI 52100 bearing steel. The total damage is divided into elastic damage related to the shear stress range and plastic damage associated with plastic deformation. Material parameters are determined based on experimental data from the literature, and validation is conducted to ensure the validity of the model. Finally, the RCF behavior of the EMU axle box bearing, including crack initiation, crack propagation, and spalling, is simulated, and reasonable results are obtained. This study provides valuable insights into the RCF behavior of EMU axle box bearings and contributes to the accurate prediction of the fatigue life.

A Lightweight Optimization Study for Large Capacity Dry-Type On-board Traction Transformer of EMU Based on CFD and RSM

Using the dry-type on-board traction transformer cooled by train-induced wind is an effective way to realize the lightweight of on-board equipment. However, the existing small capacity fan cooled dry-type on-board traction transformer cannot meet the power requirements of the high-speed electric multiple unit. For the large capacity dry-type on-board traction transformer, the lack of mathematical models that can reflect the relationship between hotspot temperature and design parameters limits its development. This paper aims to propose an optimization method for large capacity dry-type on-board traction transformer. First, the user-defined function based computational fluid dynamics modelling method for dry-type on-board traction transformer is proposed and validated to accurately obtain the winding hotspot temperature. Then, the response surface models of winding hotspot temperature are firstly proposed based on the response surface methodology. Finally, the lightweight optimization of a 6.3 MVA dry-type on-board traction transformer is completed, and the influence of design parameters on the weight reduction is studied. Under the conservative optimization strategy, the total weight of dry-type on-board traction transformer is reduced to 3650.40 kg, which is reduced by 38.55% compared with the oil cooled on-board traction transformer with the same rated capacity. This paper provides an efficient and feasible method and regular guidance for the lightweight optimal design of dry-type on-board traction transformer, and the obtained response surface models can be used for the optimization of similar dry-type on-board traction transformer under different specific needs.

Electric field distribution and performance optimization of high-speed train cable terminal with internal defects

High voltage cable terminal of high-speed electric multiple units (EMU) is an important part of on-board high voltage equipment. As the weak link of the cable assembly, the cable terminal is prone to penetrability discharge and resulting in insulation failure, which seriously affects the EMU’s safety and reliability. The electric field intensity distribution of high-voltage cable terminal with internal defects such as air gaps, moisture, semiconductors and carbons are simulated and studied. At the same time, in order to improve the distortion of the internal electric field intensity of the EMU cable terminal, nonlinear materials and high dielectric materials are used to replace the stress tube respectively, and the optimization effects of the two materials on the internal distortion electric field of the cable terminal are studied. The results show that the defects are closer to the multi-layer combination area among main insulation layer, stress tube layer and outer semiconducting layer of the cable terminal, the electric field distortion of multi-layer combination area become greater. With the increasing of defect size, the electric field intensity also gradually increases. However, the inner defect location and size have less effect on the electric field intensity along the cable terminal umbrella skirt. It is found that both nonlinear materials and high dielectric materials can effectively optimize the electric field intensity of cable terminal and improve the electric field distortion. This work can be helpful for operation and design of high-speed EMU’s cable terminal.

Development and engineering application of integrated safety monitoring systems for China's high-speed trains

Abstract With the improvement of the running speed of China's high-speed trains, the demands for running status monitoring and security assurance of High-speed Electric Multiple Units (EMU) have increased significantly. However, the current safety monitoring systems are independent, which is not conducive to the comprehensive monitoring and information sharing of the whole vehicle. The temperature monitoring of running gear is insensitive to early failures. How to develop a train operation safety monitoring system with strong engineering implementation and high integration is a key problem to be solved. For the monitoring of running stationarity, frame stability and running gear health of China's high-speed trains, an integrated safety monitoring system framework is designed, and the logic and algorithm for the diagnosis of stationarity, stability and health states of rotating parts are constructed. Monitoring software which fused the temperature, high and low frequency vibration data is developed, and the design and installation of the vibration temperature composite sensors are completed. The research results have realized the integration and comprehensive processing of multiple monitoring systems, completed the improvement from single component and vehicle-level safety monitoring to multi-system, train-level and interactive monitoring. In the process of real vehicle application, the developed monitoring system acquires the vehicle operation status data accurately and in real time. The constructed diagnosis algorithm and logic evaluates the vehicle operation status accurately and in a timely manner, and avoids the progression from fault to accident. The research results show that the integrated safety monitoring system can provide technical support for train operation safety.

A personalised operation and maintenance approach for complex products based on equipment portrait of product-service system

• A personalised OM approach for complex products is proposed. • A framework of POMA-CP based on equipment portrait within the PSS is developed. • A multi-level case library and dynamic equipment portrait model are established. • The feasibility of the proposed method is verified through an application scenario. Based on the holistic data of product-service system (PSS) delivery processes, equipment portrait can be used to describe personalised user requirements and conduct targeted analysis on the performance of complex products. Therefore, a promising application combining PSS and equipment portrait is to establish a more refined portrait model to improve the accuracy and applicability of operation and maintenance (OM) schemes for industrial products. However, studies in the above field are facing many challenges. For example, the research on equipment portrait in the industrial field is still in its infancy. PSS and equipment portrait are studied separately, and the overall solution that integrates PSS and equipment portrait for complex products OM service is almost vacant. A personalised OM approach for complex products (POMA-CP) is proposed to address these challenges. First, a framework of POMA-CP is developed to show how the processes of refined OM can be implemented. Then, a solution of POMA-CP based on the framework is designed. A multi-level case library, dynamic equipment portrait model, and case-pushing mechanism are established and developed. Active pushing of the best similar cases and automatic generation of service schemes are realised. Finally, an application scenario for a high-speed electric multiple units (EMU) bogie is presented to illustrate the feasibility and effectiveness of the proposed approach. Higher accuracy and applicability for service schemes are achieved, resulting in the efficient reusing of OM knowledge, proactive implementation of refined maintenance, and reducing maintenance cost and resource consumption.

Research on the Characteristics of Wheel Hollow Wear and Suppression Measures of High-Speed Trains

Wheel hollow wear is a serious problem of high-speed electric multiple units (HSE). The wheel wear characteristics of the CRH5 HSE are investigated using test data and simulation results. Then an HSE dynamics model and wheel wear model are established. Two assessment indicators for wheel hollow wear are proposed: the wheel wear simplification index and the contact fatigue index. Finally, some suppression measures of wheel hollow wear are proposed. The wheel wear simplification index is calculated simply and with strong engineering applicability. The fatigue index can effectively assess the fatigue level of different hollow wear. For the vehicle with the larger wheel profile hardness, the larger equivalent damping, and the smaller node stiffness of the anti-yaw damper, rail profile grinding can effectively suppress the wheel hollow wear of HSE. The research provides ideas for the design of bogies for HSE.

Experimental study of bogie instability monitoring and suppression measures for high-speed EMUs

As the key part of the rail vehicle, the bogie’s dynamic performance directly affects the vehicle’s operating performance. A large number of bogie instability phenomena occur during the operation of high-speed Electric Multiple Units (EMUs), which has an important impact on the safety and passenger comfort of the high-speed EMUs. In this paper, through a combination of line tests and simulations to study bogie hunting, line tests are first conducted to test the vibration data and wheel wear evolution of the high-speed EMUs. The data was analyzed to find out the evolution of the bogie instability law of the high-speed EMUs. Then the vehicle dynamic model is established to find out the mechanism of the bogie instability, and finally bogie instability suppression measures are proposed. The results of the test data showed that the wheel wear, the frequency and the amplitude of bogie instability of motor car is greater than that of the trailer car, and the ride index is poorer. The ride indexes at the early and late stages after wheel re-profiling do not exceed 2.5. The equivalent conicity increase is the main cause of bogie instability, and the bogie instability can be effectively suppressed by wheel re-profiling, replacing the yaw damper, and the rail grinding.