Analysis of the Temperature Characteristics of High-speed Train Bearings Based on a Dynamics Model and Thermal Network Method

High-speed trains often use temperature sensors to monitor the motion state of bearings.However,the temperature of bearings can be affected by factors such as weather and faults.Therefore,it is necessary to analyze in detail the relationship between the bearing temperature and influencing factors.In this study,a dynamics model of the axle box bearing of high-speed trains is established.The model can obtain the contact force between the rollers and raceway and its change law when the bearing contains outer-ring,inner-ring,and rolling-element faults.Based on the model,a thermal network method is introduced to study the temperature field distribution of the axle box bearings of high-speed trains.In this model,the heat generation,conduction,and dispersion of the isothermal nodes can be solved.The results show that the temperature of the contact point between the outer-ring raceway and rolling-elements is the highest.The relationships between the node temperature and the speed,fault type,and fault size are analyzed,finding that the higher the speed,the higher the node temperature.Under different fault types,the node temperature first increases and then decreases as the fault size increases.The effectiveness of the model is demonstrated using the actual temperature data of a high-speed train.This study proposes a thermal network model that can predict the temperature of each component of the bearings on a high-speed train under various speed and fault conditions.

Lumped-Parameter Thermal Network Model and Experimental Research of Interior PMSM for Electric Vehicle

A 25kW interior permanent magnet synchronous machine(IPMSM)applied to the electric vehicle is introduced in the paper.A lumped-parameter thermal network model is presented for IPMSM temperature rise calculation.Furthermore,a 3D liquid-solid coupling model considering the assembly clearance is compared with the 2D lumped-parameter thermal network model.Finally,a dynamometer platform for temperature rise measurement is established to verify the above-mentioned methods,which obtains the measured efficiency map at rated load case and overload case.At the same time,the measured no-load back electromotive Force(EMF),load line input voltage and load current are gathered.Thermocouple PTC100 is used to measure the temperature of the stator winding and iron core,and the FLUKE infrared thermal imager is applied to measure the surface temperature of PMSM and controller.Testing result shows that the lumped-parameter thermal network have a high accuracy to predict each part temperature.

Accurate 3D Thermal Network Development for Direct-drive Outer-rotor Hybrid-PM Flux-switching Generator

Heat and thermal problems are major obstacles to achieving high power density in compact permanent magnet(PM)topologies.Consequently,a comprehensive,accurate,and rapid temperature rise estimation method is required for novel electric machines to ensure safe and reliable operations.A unique three-dimensional(3D)lumped parameter thermal network(LPTN)is presented for accurate thermal modeling of a newly developed outer-rotor hybrid-PM flux switching generator(OR-HPMFSG)for direct-drive applications.First,the losses of the OR-HPMFSG are calculated using 3D finite element analysis(FEA).Subsequently,all machine components considering the thermal contact resistance,anisotropic thermal conductivity of materials,and various heat flow paths are comprehensively modeled based on the thermal resistances.In the proposed 3-D LPTN,internal nodes are considered to predict the average temperature as well as the hot spots of all active and passive components.Experimental measurements are performed on a prototype OR-HPMFSG to validate the efficiency of the 3-D LPTN.A comparison of the results at various operating points between the developed 3-D LPTN,experimental test,and FEA indicates that the 3-D LPTN quickly approximates the hotspot and mean temperature of all components under both transient and steady states with high accuracy.

Application of entransy to optimization design of parallel thermal network of thermal control system in spacecraft

For distribution optimization of the flow rate of cold fluid and heat transfer area in the parallel thermal network of the thermal control system in spacecraft,a physical and mathematical model is set up,analyzed and discussed with the entransy theory.It is found that the optimization objective of this problem and the optimization direction of the extremum entransy dissipation principle are consistent in theory.For a two-branch thermal network system,the distributions of the flow rate of the cold fluid and the heat transfer area are optimized by calculating the extremum entransy dissipation with the Newton method.The influential factors of the optimized distributions are also analyzed and discussed.The results show that the main influence factors are the heat transfer rate of the branches and the total heat transfer area.The total flow rate of the cold fluid has a threshold,beyond which further increasing its value brings very little influence on the optimization results.Moreover,the difference between the extremum entransy dissipation principle and the minimum entropy generation principle is also discussed when they are used to analyze the problem in this paper,and the extremum entransy dissipation principle is found to be more suitable.In addition,the Newton method is mathematically efficient to solve the problem,which could accomplish the optimized distribution in a very short time for a ten-branch thermal network system.

Entransy dissipation,entransy-dissipation-based thermal resistance and optimization of one-stream hybrid thermal network

The one-stream hybrid thermal network is analyzed and discussed based on the entransy theory,and the results are compared with those from the entropy generation optimization.The theoretical analysis indicates that the minimum heat-flow-weighted temperature of the thermal networks corresponds to the minimum entransy dissipation rate and the minimum thermal resistance.For a simple hybrid thermal network consisting of three thermal components,the expression of entransy dissipation is conducted,and the heat transfer area and the mass flow rate are calculated and optimized.The optimal results are obtained in order to minimize the entransy dissipation and the thermal resistance.The optimal results are calculated for various combinations,such as series connection,parallel connection and other hybrid connections.The numerical results are in accordance with the theoretical analysis.Both the theoretical analysis and the numerical results show that the minimum entransy dissipation and the minimum thermal resistance correspond to the minimum heat-flow-weighted temperature of the thermal networks while the minimum entropy generation does not.

Correlating thermal conductivity of pure hydrocarbons and aromatics via perceptron artificial neural network (PANN) method

Accurate estimation of liquid thermal conductivity is highly necessary to appropriately design equipments in different industries. Respect to this necessity, in the current investigation a feed-forward artificial neural network(ANN) model is examined to correlate the liquid thermal conductivity of normal and aromatic hydrocarbons at the temperatures range of 257–338 K and atmospheric pressure. For this purpose, 956 experimental thermal conductivities for normal and aromatic hydrocarbons are collected from different previously published literature.During the modeling stage, to discriminate different substances, critical temperature(Tc), critical pressure(Pc)and acentric factor(ω) are utilized as the network inputs besides the temperature. During the examination, effects of different transfer functions and number of neurons in hidden layer are investigated to find the optimum network architecture. Besides, statistical error analysis considering the results obtained from available correlations and group contribution methods and proposed neural network is performed to reliably check the feasibility and accuracy of the proposed method. Respect to the obtained results, it can be concluded that the proposed neural network consisted of three layers namely, input, hidden and output layers with 22 neurons in hidden layer was the optimum ANN model. Generally, the proposed model enables to correlate the thermal conductivity of normal and aromatic hydrocarbons with absolute average relative deviation percent(AARD), mean square error(MSE), and correlation coefficient(R^2) of lower than 0.2%, 1.05 × 10^(-7) and 0.9994, respectively.

Thermal Defect Analysis on Transformer Using a RLC Network and Thermography

Electrical transformers are vital components found virtually in most power-operated equipments. These transformers spontaneously radiate heat in both operation and steady-state mode. Should this thermal radiation inherent in transformers rises above allowable threshold a reduction in efficiency of operation occurs. In addition, this could cause other components in the system to malfunction. The aim of this work is to detect the remote causes of this undesirable thermal rise in transformers such as oil distribution transformers and ways to control this prevailing thermal problem. Oil transformers consist of these components: windings usually made of copper or aluminum conductor, the core normally made of silicon steel, the heat radiators, and the dielectric materials such as transformer oil, cellulose insulators and other peripherals. The Resistor-Inductor-Capacitor Thermal Network (RLCTN) model at architectural level identifies with these components to have ensemble operational mode as oil transformer. The Inductor represents the windings, the Resistor representing the core and the Capacitor represents the dielectrics. Thermography of transformer under various loading conditions was analyzed base on Infrared thermal gradient. Mathematical, experimental, and simulation results gotten through RLCTN with respect to time and thermal image analysis proved that the capacitance of the dielectric is inversely proportional to the thermal rise.

Fuzzy neural network analysis on gray cast iron with high tensile strength and thermal conductivity

To develop a high performance gray cast iron with high tensile strength and thermal conductivity, multivariable analysis of microstructural effects on properties of gray cast iron was performed. The concerned parameters consisted of graphite content, maximum graphite length, primary dendrite percentage and microhardness of the matrix. Under the superposed influence of various parameters, the relationships between thermal conductivity and structural characteristics become irregular, as well as the effects of graphite length on the strength. An adaptive neuro-fuzzy inference system was built to link the parameters and properties. A sensitivity test was then performed to rank the relative impact of parameters. It was found that the dominant parameter for tensile strength is graphite content, while the most relative parameter for thermal conductivity is maximum graphite length. The most effective method to simultaneously improve the tensile and thermal conductivity of gray cast iron is to reduce the carbon equivalent and increase the length of graphite flakes.

A thermal flux-diffusing model for complex networks and its applications in community structure detection

We introduce a thermal flux-diffusing model for complex networks. Based on this model, we propose a physical method to detect the communities in the complex networks. The method allows us to obtain the temperature distribution of nodes in time that scales linearly with the network size. Then, the local community enclosing a given node can be easily detected for the reason that the dense connections in the local communities lead to the temperatures of nodes in the same community being close to each other. The community structure of a network can be recursively detected by randomly choosing the nodes outside the detected local communities. In the experiments, we apply our method to a set of benchmarking networks with known pre-determined community structures. The experiment results show that our method has higher accuracy and precision than most existing globe methods and is better than the other existing local methods in the selection of the initial node. Finally. several real-world networks are investigated.

Analysis of thermal conductivity in tree-like branched networks

Asymmetric tree-like branched networks are explored by geometric algorithms. Based on the network, an analysis of the thermal conductivity is presented. The relationship between effective thermal conductivity and geometric structures is obtained by using the thermal-electrical analogy technique. In all studied cases, a clear behaviour is observed, where angle （δ,θ） among parent branching extended lines, branches and parameter of the geometric structures have stronger effects on the effective thermal conductivity. When the angle δ is fixed, the optical diameter ratio β＋ is dependent on angle θ. Moreover, γand m are not related to β＊. The longer the branch is, the smaller the effective thermal conductivity will be. It is also found that when the angle θ〈δ2, the higher the iteration m is, the lower the thermal conductivity will be and it tends to zero, otherwise, it is bigger than zero. When the diameter ratio β1 〈 0.707 and angle δ is bigger, the optimal k of the perfect ratio increases with the increase of the angle δ; when β1 〉 0.707, the optimal k decreases. In addition, the effective thermal conductivity is always less than that of single channel material. The present results also show that the effective thermal conductivity of the asymmetric tree-like branched networks does not obey Murray＇s law.

Wavelet Neural Network Based on NARMA-L2 Model for Prediction of Thermal Characteristics in a Feed System

Research of thermal characteristics has been a key issue in the development of high-speed feed system. Most of the work carried out thus far is based on the principle of directly mapping the thermal error against the temperature of critical machine elements irrespective of the operating conditions. But recent researches show that different sets of operating parameters generated significantly different error values even though the temperature of the machine elements generated was similar. As such, it is important to develop a generic thermal error model which is capable of evaluating the positioning error induced by different operating parameters. This paper ultimately aims at the development of a comprehensive prediction model that can predict the thermal characteristics under different operating conditions （feeding speed, load and preload of ballscrew） in a feed system. A novel wavelet neural network based on feedback linearization autoregressive moving averaging （NARMA-L2） model is introduced to predict the temperature rise of sensitive points and thermal positioning errors considering the different operating conditions as the model inputs. Particle swarm optimization（PSO） algorithm is brought in as the training method. According to ISO230-2 Positioning Accuracy Measurement and ISO230-3 Thermal Effect Evaluation standards, experiments under different operating conditions were carried out on a self-made quasi high-speed feed system experimental bench HUST-FS-001 by using Pt100 as temperature sensor, and the positioning errors were measured by Heidenhain linear grating scale. The experiment results show that the recommended method can be used to predict temperature rise of sensitive points and thermal positioning errors with good accuracy. The work described in this paper lays a solid foundation of thermal error prediction and compensation in a feed system based on varying operating conditions and machine tool characteristics.

CNC Thermal Compensation Based on Mind Evolutionary Algorithm Optimized BP Neural Network

Thermal deformation error is one of the most important factors affecting the CNCs’ accuracy, so research is conducted on the temperature errors affecting CNCs’ machining accuracy;on the basis of analyzing the unpredictability and pre-maturing of the results of the genetic algorithm, as well as the slow speed of the training speed of the particle algorithm, a kind of Mind Evolutionary Algorithm optimized BP neural network featuring extremely strong global search capacity was proposed;type KVC850MA/2 five-axis CNC of Changzheng Lathe Factory was used as the research subject, and the Mind Evolutionary Algorithm optimized BP neural network algorithm was used for the establishment of the compensation model between temperature changes and the CNCs’ thermal deformation errors, as well as the realization method on hardware. The simulation results indicated that this method featured extremely high practical value.

Real Time Thermal Image Based Machine Learning Approach for Early Collision Avoidance System of Snowplows

In an effort to reduce vehicle collisions with snowplows in poor weather conditions, this paper details the development of a real time thermal image based machine learning approach to an early collision avoidance system for snowplows, which intends to detect and estimate the distance of trailing vehicles. Due to the operational conditions of snowplows, which include heavy-blowing snow, traditional optical sensors like LiDAR and visible spectrum cameras have reduced effectiveness in detecting objects in such environments. Thus, we propose using a thermal infrared camera as the primary sensor along with machine learning algorithms. First, we curate a large dataset of thermal images of vehicles in heavy snow conditions. Using the curated dataset, two machine-learning models based on the modified ResNet architectures were trained to detect and estimate the trailing vehicle distance using real-time thermal images. The trained detection network was capable of detecting trailing vehicles 99.0% of the time at 1500.0 ft distance from the snowplow. The trained trailing distance network was capable of estimating distance with an average estimation error of 10.70 ft. The inference performance of the trained models is discussed, along with the interpretation of the performance.

Characteristic analysis of mechanical thermal coupling model for bearing rotor system of high-speed train

Based on Newton’s second law and the thermal network method,a mechanical thermal coupling model of the bearing rotor system of high-speed trains is established to study the interaction between the bearing vibration and temperature.The influence of lubrication on the vibration and temperature characteristics of the system is considered in the model,and the real-time relationship between them is built up by using the transient temperature field model.After considering the lubrication,the bearing outer ring vibration acceleration and node temperature considering grease are lower,which shows the necessity of adding the lubrication model.The corresponding experiments for characteristics of vibration and temperature of the model are respectively conducted.In the envelope spectrum obtained from the simulation signal and the experimental signal,the frequency values corresponding to the peaks are close to the theoretical calculation results,and the error is very small.In the three stages of the temperature characteristic experiment,the node temperature change of the simulation model is consistent with the experiment.The good agreement between simulation and experiments proves the effectiveness of the model.By studying the influence of the bearing angular and fault size on the system node temperature,as well as the change law of bearing lubrication characteristics and temperature,it is found that the worse the working condition is,the higher the temperature is.When the ambient temperature is low,the viscosity of grease increases,and the oil film becomes thicker,which increases the sliding probability of the rolling element,thus affecting the normal operation of the bearing,which explains the phenomenon of frequent bearing faults of high-speed trains in the low-temperature area of Northeast China.Further analysis shows that faults often occur in the early stage of train operation in the low-temperature environment.

高功率密度永磁电机磁热双向耦合分析

针对电动汽车永磁电机功率密度提高,热负荷增加温升增大,影响电机运行性能和可靠性的问题,基于三维等效热网络法对高功率密度永磁电机进行磁热双向耦合分析。建立电机三维等效热网络模型,计算指定节点温度。将电磁场有限元模型和热网络模型结合,进行磁热双向耦合计算,求解温度分布,确定电机冷却效果。最后利用流固耦合温度场仿真,分析了两者温升计算的差异。基于三维等效热网络法的磁热耦合温度计算准确性较高,且计算时间大幅度缩短,对计算机备置要求较低,更适用于高密度电机初期结构设计。

基于图计算的可重构电池网络能效提升与热安全管控

可重构电池系统凭借其能量信息耦合优势成为解决电化学储能“木桶效应”的一个有效手段。但其可用路径规划量会随着电池数目的增多而成倍增长,使得在路径选择时需要考虑不同拓扑结构和负载需求约束,从而使重构策略难以快速制定和应用。该文利用可重构电池网络与图的一致性,将电池网络动态控制问题转化为图的遍历寻优问题,提出了基于记忆化搜索的图深度优先遍历的重构电池网络能效提升及热安全管控策略,从而获取了满足拓扑约束条件的最优开关配置,并可排除温度异常的故障电池模组。最后,通过搭建可重构电池网络实验平台对该方法进行了验证。该研究结果能够为提高电池网络系统能效及安全性提供一定借鉴。

Analysis of temperature field for a surface-mounted and interior permanent magnet synchronous motor adopting magnetic-thermal coupling method

Aiming at obtaining high power density of surface-mounted and interior permanent magnet synchronous motor(SIPMSM),it is important to accurately calculate the temperature field distribution of SIPMSM,and a magnetic-thermal coupling method is proposed.The magnetic-thermal coupling mechanism is analyzed.The thermal network model and finite element model are built by this method,respectively.The effects of power frequency on iron losses and temperature fields are analyzed by the magnetic-thermal coupling finite element model under the condition of rated load,and the relationship between the load and temperature field is researched under the condition of the synchronous speed.In addition,the equivalent thermal network model is used to verify the magnetic-thermal coupling method.Then the temperatures of various nodes are obtained.The results show that there are advantages in both computational efficiency and accuracy for the proposed coupling method,which can be applied to other permanent magnet motors with complex structures.

低密度高回弹导热垫片的制备

以自制双组分导热凝胶为基料,引入高导热球形氮化硼和片状氢氧化铝作为导热组分,与球形氧化铝互配构筑复合导热网络,制得低密度高回弹导热垫片,研究了复配导热填料配方对导热垫片性能的影响。结果表明,将高导热、低密度的球形氮化硼及片状氢氧化铝引入复配导热填料体系中,可在保证导热垫片具备较高热导率的同时,显著降低导热垫片密度。但两者过量添加会导致导热垫片的柔韧性降低,表现为压缩强度提高、压缩永久变形率增大。因此,针对车用电子器件散热的应用场景,采用适量氧化铝替代部分氮化硼和氢氧化铝填料(氢氧化铝、球形氧化铝和球形氮化硼按质量比60.8:16.0:2.4复配)构筑导热网络,可以获得最优综合性能,所得导热垫片热导率为1.63W/(m·K),密度为1.85g/cm^(3),压缩率30%和50%时的压缩永久变形率为8.5%和28.3%。