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key Factors Identification for the Energy Efficiency of Metro System Based on DEMATEL-ISM 1

2nd International Conference on Information Control, Electrical engineering and rail transit, ICEERT 2022

作者： Zhang, Chen Chen Xiao, Xue Mei Wang, Yan Hui School of Mechanical and Automotive Engineering Xiamen University of Technology Xiamen361024 China State Key Laboratory of Rail Transit Control and Safety Beijing Jiaotong University Beijing100044 China

ISBN: (数字)9789819964314

ISBN: (纸本)9789819964307

A quantitative analysis method for identifying key factors influencing the energy efficiency of the metro system based on DEMATEL-ISM was proposed in response to the complex internal relationship of key factors. To be specific, a system of factors influencing the energy efficiency was established in terms of vehicles, operation organizations, and electromechanical equipment, with a focus on analysing the interactive relationship between the influencing factors. On this basis, a DEMATEL-ISM model method system was put forward to analyse the importance of various factors using the centrality and cause degree of the influencing factors. Moreover, a multi-level hierarchical model for the factors influencing the energy efficiency was also built. Finally, the set composed of key factors of energy efficiency of the metro system was determined, providing theoretical support for the energy-saving optimization and system plan and design. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Energy efficiency

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Effect of warm rolling on microstructure and mechanical properties of Fe_(50)Mn_(30)Co_(10)Cr_(10) high-entropy alloy

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Journal of Central South University 2024年第11期31卷 4060-4081页

作者： JIAO Hai-tao WU Wen-sheng HOU Zong-bo CHEN Zhao-xia ZHAO Zi-long TANG Yan-chuan ZHANG Yuan-xiang ZHAO Long-zhi State Key Laboratory of Performance Monitoring Protecting of Rail Transit Infrastructure East China Jiaotong UniversityNanchang 330013China School of Chemical Engineering and Technology Sun Yat-sen UniversityZhuhai 519082China State Key Laboratory of Rolling and Automation Northeastern UniversityShenyang 110819China

In this study,specific warm rolling was carried out to process the Fe_(50)Mn_(30)Co_(10)Cr_(10) high-entropy *** aim was to investigate the effect of warm rolling temperature on the microstructure and mechanical *** results indicated that serious transverse cracks appeared in the 25℃ rolled sheet with reduction of 60%,which was significantly improved through 100−300℃ warm *** addition,the increase of rolling temperature promoted dislocation slip and inhibited martensitic transformation and twinning deformation.A single face centered cubic(FCC)matrix with abundant dislocations and stacking faults was developed in the 300℃rolled ***,the deformation stored energy gradually increased,and the copper-type deformation texture was gradually *** annealing,the recrystallized microstructure of 25−200℃ rolled sheets was composed of FCC and a small amount of HCP ***,the hexagonal close packed(HCP)content in the annealed sheet rolled at 300℃ was as high as 20%−23% after annealing for 2−4 min and decreased to 4.5%after annealing for 8 *** recrystallized microstructure contained a large number of annealing twins,and the average grain size increased with the increase of rolling ***,the mechanical properties of the annealed sheet were significantly improved after warm rolling.

关键词： Fe_(50)Mn_(30)Co_(10)Cr_(10)alloy warm rolling microstructure texture mechanical properties

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A Degradation Trend Dynamic Prediction Method for Rolling Bearing Health Status Evaluation 1

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UNIfied Conference of International Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2023, International Conference on Maintenance engineering, IncoME-V 2023, International conference on the Efficiency and Performance engineering Network, TEPEN 2023

作者： Qiao, Hui Chen, Zhenyu Chen, Wenhao Zhang, Ying Huang, Zhao Pan, Huafei Ding, Xiaoxi Xi’an Rail Transit Investment and Development Co. Ltd. Xi’an710018 China CAEP Institute of Electronic Engineering Mianyang621900 China State Key Laboratory of Mechanical Transmission Chongqing University Chongqing400044 China

ISBN: (数字)9783031494130

ISBN: (纸本)9783031494123

Rolling bearings as a key component of the high-end equipment, where there is a demand that the health status of rolling bearings should be evaluated timely and accurately for preventive maintenance. However, the non-smoothness and non-linearity of the collection signal make it challenging to characterize bearing degradation and extract degradation patterns. Additionally, the diversity of bearing degradation makes it difficult to classify and predict the degradation process. To address the appeal issues, this study is mainly carried out from two aspects: health index construction and degradation trend dynamic forecasting. Firstly, a healthy index with strong characteristics is conducted from the high-dimensional sensitive feature space. Secondly, the support vector data description (SVDD) method is used to identify the early degradation point. The dynamic threshold is later determined by the FPLR method for the historical data of the early degradation point to the current observation point. Finally, an autoregressive integrated moving average model (ARIMA) model is trained to dynamically predict future degradation trends. The aforementioned methods have been validated using publicly available datasets, and they hold significant importance in achieving intelligent warning and alarm systems for bearings, as well as improving the accuracy of remaining life prediction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Alarm systems

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Site Selection Planning of railway Rescue Train in a Mountainous Area of China 3

Site Selection Planning of Railway Rescue Train in a Mountai...

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3rd International Conference on rail Transportation, ICRT 2024

作者： Wang, Shuzhen Bai, Yanbo Cui, Chongyang He, Zhenxing Wang, Kaiyun Yun, Jianfeng Vibration and Noise Control Laboratory School of Mechanical Engineering Lanzhou Jiaotong Univ. China Dept. of Locomotive Engineering Liaoning Railway Vocational and Technical College China Train and Track Research Institute State Key Laboratory of Rail Transit Vehicle System Southwest Jiaotong Univ. China

ISBN: (数字)9780784485941

ISBN: (纸本)9780784485941

Based on the set coverage theory and the multi-objective site selection planning theory, a multi-objective site selection planning model for rescue trains of a railway in mountainous area of China is established. The results show that when four rescue trains are planned to be deployed, two schemes can be obtained, and the average rescue response time of the two schemes is not much different. When five rescue trains are planned to be deployed, five schemes are obtained, of which the average rescue response time of four schemes is very close, and they can be considered as reasonable schemes. When six or more rescue trains are planned to be deployed, the deployment density of rescue trains is too high, resulting in redundancy of rescue resources. Compared with the single objective site selection planning model, the scheme obtained by the multi-objective site selection planning model of the railway in mountainous area of China rescue train can greatly reduce the average rescue response time and improve the rescue efficiency. © ASCE.

关键词： railroads

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Online Monitoring Method for Insulator Self-explosion Based on Edge Computing and Deep Learning

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CSEE Journal of Power and Energy Systems 2022年第6期8卷 1684-1696页

作者： Baoquan Wei Zhongxin Xie Yande Liu Kaiyun Wen Fangming Deng Pei Zhang State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure East China Jiaotong UniversityNanchang 330013China School of Mechatronics and Vehicle Engineering East China Jiaotong UniversityNanchang 330013China School of Electrical Engineering Beijing Jiaotong UniversityBeijing 10091China

Aiming at the problems of traditional centralized cloud computing which occupies large computing resources and creates high latency,this paper proposes a fault detection scheme for insulator self-explosion based on edge computing and DL(deep learning).In order to solve the high amount of computation brought by the deep neural network and meet the limited computing resources at the edge,a lightweight SSD(Single Shot MultiBox Detector)target recognition network is designed at the edge,which adopts the MobileNets network to replace VGG16 network in the original model to reduce redundant *** the cloud,three detection algorithms(Faster-RCNN,Retinanet,YOLOv3)with obvious differences in detection performance are selected to obtain the coordinates and confidence of the insulator self-explosion area,and then the self-explosion fault detection of the overhead transmission line is realized by a novel multimodel fusion *** experimental results show that the proposed scheme can effectively reduce the amount of uploaded data,and the average recognition accuracy of the cloud is 95.75%.In addition,it only increases the power consumption of edge devices by about 25.6W/h in their working *** with the existing online monitoring technology of insulator selfexplosion at home and abroad,the proposed scheme has the advantages of low transmission delay,low communication cost and high diagnostic accuracy,which provides a new idea for online monitoring research of power internet of things equipment.

关键词： Deep learning edge computing insulator self-explosion online monitoring power inspection

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Advancements in dynamic characteristics analysis of superconducting electrodynamic suspension systems: Modeling, experiment, and optimization

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Superconductivity 2024年第3期11卷 51-65页

作者： Huan Huang Haitao Li Tim Coombs Hanlin Zhu Yougang Sun Guobin Lin Junqi Xu Jun Zheng Key Laboratory of Railway Industry of Maglev Technology Tongji UniversityShanghai 201804China National Maglev Transportation Engineering R&D Center Tongji UniversityShanghai 201804China State Key Laboratory of Rail Transit Vehicle System Southwest Jiaotong UniversityChengdu 610031China Department of Electrical Engineering University of CambridgeCambridge CB30FAUK Department of Electrical&Electronic Engineering University of BristolBristol BS81UBUK

Superconducting electrodynamic suspension (EDS) presents numerous advantages, including large suspension gaps, high lift-to-drag ratios, and lower requirements for track irregularities. Recent advancements in superconducting materials have further enhanced the feasibility of this technology, and hence multiple research institutions are actively developing and improving this high-speed rail technology. Superconducting EDS achieves passive suspension and guidance by the interaction between ground null-flux coils and onboard superconducting magnets, forming an electromechanical coupled system. Thus, electromechanical coupling modeling and equivalent experimental methods are essential in evaluating and optimizing this system. This article reviews the research on dynamic characteristics analysis of superconducting EDS, focusing on modeling and experimental methods. Firstly, it revisits the development history of superconducting EDS and the new opportunities brought by advancements in superconducting materials. Secondly, it discusses various modeling approaches for the suspension system, emphasizing their benefits and limitations. Thirdly, it describes equivalent experimental methods and their respective application scenarios. Then, it reviews important conclusions and possible optimization methods related to dynamic performance and electromechanical coupling research. Additionally, the sliding window method is introduced to improve computational efficiency in vehicle dynamics modeling. This article provides insights into the current state and future directions of superconducting EDS research, serving as a valuable reference for researchers and engineers.

关键词： Superconducting electrodynamic suspension Electromechanical coupling dynamics Equivalent experimental methods Electromagnetic damping Coil track irregularities

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侧风作用下高速磁浮列车-轨道梁耦合动力响应分析

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Journal of Central South University 2023年第8期30卷 2757-2771页

作者：田祥富向活跃陈绪黎李永乐 State Key Laboratory of Rail Transit Vehicle System(Southwest Jiaotong University) Chengdu 610031China Department of Bridge Engineering Southwest Jiaotong UniversityChengdu 610031China Wind Engineering Key Laboratory of Sichuan Province Chengdu 610031China

侧风作用是影响高速磁浮系统动力设计的重要因素之一。为获得侧风作用下高速磁浮列车-轨道梁耦合振动响应,首先,建立了风-静悬磁浮列车-轨道梁(WSMG)和风-移动磁浮列车-轨道梁(WMMG)空间耦合分析模型。然后,通过风洞试验对磁浮车辆的气... 详细信息

侧风作用是影响高速磁浮系统动力设计的重要因素之一。为获得侧风作用下高速磁浮列车-轨道梁耦合振动响应,首先,建立了风-静悬磁浮列车-轨道梁(WSMG)和风-移动磁浮列车-轨道梁(WMMG)空间耦合分析模型。然后,通过风洞试验对磁浮车辆的气动特性进行测试。磁浮列车的控制系统采用比例-积分-微分(PID)控制器和加速度反馈相结合的比例-积分-微分-加速度(PIDA)控制算法进行调控。最后,分析了平均风、脉动风、风速和车速对磁浮系统动力响应的影响。结果表明:PIDA控制算法可以消除侧风引起的磁浮间隙稳态误差,并减小大约40%的车体横向位移;均匀风只改变列车的平衡位置,脉动风和轨道不平整是引起磁浮列车-轨道系统振动的主要原因;风速对车辆的横向振动的影响更为敏感,当风速超过30 m/s时,高速磁浮列车应停止运行。

关键词：风-磁浮列车-轨道梁风洞试验控制系统侧向风动力响应

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Integrated Optimization of Energy-Efficient Timetable and Speed Profiles for Train Platoons in Urban rail transit Systems 27

Integrated Optimization of Energy-Efficient Timetable and Sp...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Li, Haodong Yin, Jiateng Tang, Tao D'Ariano, Andrea You, Mengxing Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation Beijing100044 China Roma Tre Universily Department of Civi Computer Science and Aeronautical Technologies Engineering Rome0018511 Italy Nanchang Rail Transit Group Co. Ltd. Jiangxi330038 China

ISBN: (纸本)9798331505929

Train platooning, which allows multiple train units to be virtually coupled into a platoon with very short following distances, has become an emerging technology in railway industry. Our study investigates the energy-efficient operation of train platoons to reduce the energy consumption of trains while satisfying time-varying passenger demands in practice. Specifically, we construct a mixed integer programming (MIP) model in order to simultaneously optimize the timetable and speed profiles of train platoons in an urban rail transit line. The aim of the developed MIP model is to minimize the total energy consumption of trains as well as the waiting time of traveling passengers, while satisfying the practical constraints, such as the train movement constraints, passenger loading constraints for different train platoons, etc. In order to solve the MIP model more efficiently, we develop a hybrid iterative algorithm that combines a Deep Q-Network (DQN) and an MIP solver, in which the DQN generates energy-efficient speed profiles for different types of platoons and the MIP solver then determines the optimal train schedule. Finally, we conduct real-world case studies on the Beijing subway Yizhuang line to verify the effectiveness of our approach. The results indicate that the energy-efficient operation of train platoons can achieve a better tradeoff between service quality and system energy consumption. © 2024 IEEE.

关键词： Integer programming

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A novel insulator defect detection scheme based on Deep Convolutional Auto-Encoder for small negative samples

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High Voltage 2022年第5期7卷 925-935页

作者： Fangming Deng Wei Luo Baoquan Wei Yong Zuo Han Zeng Yigang He State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure East China Jiaotong UniversityNanchangChina School of Electrical and Automation Engineering East China Jiaotong UniversityNanchangChina School of Electrical Engineering and Automation Wuhan UniversityWuhanChina

This paper presents a novel insulator defect detection scheme based on Deep Convolutional Auto-Encoder(DCAE)for small negative *** proposed DCAE scheme combines the advantages of supervised learning and unsupervised *** order to reduce the high cost of training Deep Neural Networks,this paper pre-trained the Convolutional Neural Networks(CNN)through open labelled *** transferring learning,the encoder part of the traditional Convolutional Auto-Encoder was replaced by the first three layers of the CNN,and a small number of defect samples were used to fine-tune the parameters.A threshold discrimination scheme was designed to evaluate the model detection,realising the self-explosion detection of insulator by judging the residual result and abnormal *** experimental results show that compared with the existing insulator self-explosion detection schemes,the proposed scheme can reduce the model training time by up to 40%,and the recognition accuracy can reach 97%.Moreover,this model does not need a large number of insulator labelled data and is especially suitable for small negative sample application.

关键词： scheme insulator defect

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Local Embedded Sensing-Based Gear Fault Diagnosis Under Speed Varying Condition

Local Embedded Sensing-Based Gear Fault Diagnosis Under Spee...

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作者： Wu, Shanshan Qiao, Hui Ding, Xiaoxi Chen, Wenhao Huang, Zhao Zhao, Linhua College of Mechanical and Vehicle Engineering Chongqing University Chongqing400044 China State Key Laboratory of Mechanical Transmission Chongqing University Chongqing400044 China Xi’an Rail Transit Investment and Development Co. Ltd. Xi’an China

ISBN: (纸本)9783031494208

The external signals of the gearbox sensed by the sensors are always affected by structural noise interference and path-transfer attenuation, and this would cause the fault characteristics cannot be effectively identified. Especially, those desired features would be further interfered by variable speed conditions. Focusing on these issues, this study introduced a diagnostic enhancement method via local embedded sensing (LES) system, where a vibration acceleration sensor is directly embedded in the rotational position to sense the internal excitation and response. Different from the conventional approach of external sensing system, two physical information, including vibration and speed, can be simultaneously sensed. Thereupon, a fault diagnosis method based on order analysis is later processed for the local embedded signal of gear. This diagnostic enhancement method for variable speed conditions mainly contains three steps. First, according to the characteristics of local embedded sensing signals, low-pass and high-pass digital filters are used to separate the low-frequency speed component and the high-frequency component. Then, the Hilbert transform is performed on the low-frequency speed component to get the phase curve, and then the least squares method is used to fit the phase curve to obtain the fitting phase curve. Finally, the angular domain of the high-frequency vibration signal is resampled by fitting the phase curve, and then the envelope order of the resampled angular domain signal is analyzed to extract the fault characteristics of the signal. The effectiveness of the proposed method has been verified by simulation analysis of local embedded signal of the gear. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Failure analysis

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