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Vibration-Based Fault Diagnosis for Railway Point Machines Using VMD and Multiscale Fluctuation-Based Dispersion Entropy

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Chinese Journal of Electronics 2024年第3期33卷 803-813页

作者： Yongkui SUN Yuan CAO Peng LI Guo XIE Tao WEN Shuai SU National Engineering research center of Rail transportation Operation and Control system Beijing Jiaotong University School of Electronic and Information Engineering Beijing Jiaotong University Shaanxi Key Laboratory of Complex system Control and intelligent Information Processing Xi'an University of Technology Frontiers Science center for Smart High-speed Railway system Beijing Jiaotong University

As one of the most important railway signaling equipment,railway point machines undertake the major task of ensuring train operation safety.Thus fault diagnosis for railway point machines becomes a hot topic.Considering the advantage of the anti-interference characteristics of vibration signals,this paper proposes an novel intelligent fault diagnosis method for railway point machines based on vibration signals.A feature extraction method combining variational mode decomposition(VMD) and multiscale fluctuation-based dispersion entropy is developed,which is verified a more effective tool for feature selection.Then,a two-stage feature selection method based on Fisher discrimination and ReliefF is proposed,which is validated more powerful than single feature selection methods.Finally,support vector machine is utilized for fault diagnosis.Experiment comparisons show that the proposed method performs best.The diagnosis accuracies of normal-reverse and reverse-normal switching processes reach 100% and 96.57% respectively.Especially,it is a try to use new means for fault diagnosis on railway point machines,which can also provide references for similar fields.

关键词： Fault diagnosis Vibrations Support vector machines Diversity reception Switches Feature extraction Rail transportation

Analysis and Optimization of Transient Mode Switching Behavior for Power Split Hybrid Electric Vehicle with Clutch Collaboration

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Automotive Innovation 2024年第1期7卷 150-165页

作者： Dehua Shi Sheng Liu Yujie Shen Shaohua Wang Chaochun Yuan Long Chen Automotive Engineering research Institute Jiangsu UniversityZhenjiang 212013China Jiangsu Province Engineering research center of Electric Drive system and intelligent Control for Alternative Vehicles Zhenjiang 212013China

The power split hybrid electric vehicle(HEV)adopts a power coupling configuration featuring dual planetary gearsets and multiple clutches,enabling diverse operational modes through clutch engagement and disengagement.The multi-clutch configuration usually involves the collaboration of two clutches during the transient mode switching process,thereby substantially elevating control complexity.This study focuses on power split HEVs that integrate multi-clutch mechanisms and investigates how different clutch collaboration manners impact the characteristics of transient mode switching.The powertrain model for the power-split HEV is established utilizing matrix-based methodologies.Through the formulation of clutch torque curves and clutch collaboration models,this research systematically explores the effects of clutch engagement timing and the duration of clutch slipping state on transient mode switching behaviors.Building upon this analysis,an optimization problem for control parameters pertaining to the two collaborative clutches is formulated.The simulated annealing algorithm is employed to optimize these control parameters.Simulation results demonstrate that the clutch collaboration manners have a great influence on the transient mode switching performance.Compared with the pre-calibrated benchmark and the optimal solution derived by the genetic algorithm,the maximal longitudinal jerk and clutch slipping work during the transient mode switching process is reduced obviously with the optimal control parameters derived by the simulated annealing algorithm.The study provides valuable insights for the dynamic coordinated control of the power-split HEVs featuring complex clutch collaboration mechanisms.

关键词： Power split hybrid electric vehicle Transient mode switching Clutch collaboration Simulated annealing Powertrain model

The Investigation of Data Voting Algorithm for Train Air-Braking system Based on Multi-Classification SVM and ANFIS

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Chinese Journal of Electronics 2024年第1期33卷 274-281页

作者： Juhan WANG Ying GAO Yuan CAO Tao TANG School of Electronics and Information Engineering Beijing Jiaotong University Graduate Department China Academy of Railway Science National Engineering research center of Rail transportation Operation and Control system Beijing Jiaotong University

The pressure data of the train air braking system is of great significance to accurately evaluate its operation state. In order to overcome the influence of sensor fault on the pressure data of train air braking system, it is necessary to design a set of sensor fault-tolerant voting mechanism to ensure that in the case of a pressure sensor fault, the system can accurately identify and locate the position of the faulty sensor, and estimate the fault data according to other normal data. A fault-tolerant mechanism based on multi-classification support vector machine(SVM) and adaptive network-based fuzzy inference system(ANFIS) is introduced. Multi-classification SVM is used to identify and locate the system fault state, and ANFIS is used to estimate the real data of the fault sensor. After estimation, the system will compare the real data of the fault sensor with the ANFIS estimated data. If it is similar,the system will recognize that there is a false alarm and record it. Then the paper tests the whole mechanism based on the real data. The test shows that the system can identify the fault samples and reduce the occurrence of false alarms.

关键词： Multi-classification support vector machine Adaptive network-based fuzzy inference system Train air braking system Fault-tolerant voting Multi-sensors

Probabilistic-Ellipsoid Hybrid Reliability Multi-Material Topology Optimization Method Based on Stress Constraint

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 757-792页

作者： Zibin Mao Qinghai Zhao Liang Zhang College of Mechanical and Electrical Engineering Qingdao UniversityQingdao266071China National Engineering research center for intelligent Electrical Vehicle Power system Qingdao UniversityQingdao266071China

This paper proposes a multi-material topology optimization method based on the hybrid reliability of the probability-ellipsoid model with stress constraint for the stochastic uncertainty and epistemic uncertainty of mechanical loads in optimization design.The probabilistic model is combined with the ellipsoidal model to describe the uncertainty of mechanical loads.The topology optimization formula is combined with the ordered solid isotropic material with penalization(ordered-SIMP)multi-material interpolation model.The stresses of all elements are integrated into a global stress measurement that approximates the maximum stress using the normalized p-norm function.Furthermore,the sequential optimization and reliability assessment(SORA)is applied to transform the original uncertainty optimization problem into an equivalent deterministic topology optimization(DTO)problem.Stochastic response surface and sparse grid technique are combined with SORA to get accurate information on the most probable failure point(MPP).In each cycle,the equivalent topology optimization formula is updated according to the MPP information obtained in the previous cycle.The adjoint variable method is used for deriving the sensitivity of the stress constraint and the moving asymptote method(MMA)is used to update design variables.Finally,the validity and feasibility of the method are verified by the numerical example of L-shape beam design,T-shape structure design,steering knuckle,and 3D T-shaped beam.

关键词： Stress constraint probabilistic-ellipsoid hybrid topology optimization reliability analysis multi-material design

Low-Rank Optimal Transport for Robust Domain Adaptation

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1667-1680页

作者： Bingrong Xu Jianhua Yin Cheng Lian Yixin Su Zhigang Zeng IEEE the School of Automation Wuhan University of TechnologyWuhan 430070China intelligent transportation systems research center Wuhan University of TechnologyWuhan 430063 Chongqing research Institute Wuhan University of TechnologyChongqingChina the School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074 the Key Laboratory of Image Processing and intelligent Control of Education Ministry of China Wuhan 430074China

When encountering the distribution shift between the source(training) and target(test) domains, domain adaptation attempts to adjust the classifiers to be capable of dealing with different domains. Previous domain adaptation research has achieved a lot of success both in theory and practice under the assumption that all the examples in the source domain are welllabeled and of high quality. However, the methods consistently lose robustness in noisy settings where data from the source domain have corrupted labels or features which is common in reality. Therefore, robust domain adaptation has been introduced to deal with such problems. In this paper, we attempt to solve two interrelated problems with robust domain adaptation:distribution shift across domains and sample noises of the source domain. To disentangle these challenges, an optimal transport approach with low-rank constraints is applied to guide the domain adaptation model training process to avoid noisy information influence. For the domain shift problem, the optimal transport mechanism can learn the joint data representations between the source and target domains using a measurement of discrepancy and preserve the discriminative information. The rank constraint on the transport matrix can help recover the corrupted subspace structures and eliminate the noise to some extent when dealing with corrupted source data. The solution to this relaxed and regularized optimal transport framework is a convex optimization problem that can be solved using the Augmented Lagrange Multiplier method, whose convergence can be mathematically proved. The effectiveness of the proposed method is evaluated through extensive experiments on both synthetic and real-world datasets.

关键词： Domain adaptation low-rank constraint noise corruption optimal transport

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Plotinus:A Satellite Internet Digital Twin system

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Journal of Communications and Information Networks 2024年第1期9卷 24-33页

作者： Yue Gao Kun Qiu Zhe Chen Wenjun Zhu Qi Zhang Handong Luo Quanwei Lin Ziheng Yang Wenhao Liu The intelligent Networking and Computing research center and School of Computer Science Fudan UniversityShanghai 200433China

The development of space-air-ground integrated networks (SAGIN) requires sophisticated satellite Internet emulation tools that can handle complex, dynamic topologies and offer in-depth analysis. Existing emulation platforms struggle with challenges like the need for detailed implementation across all network layers, real-time response, and scalability. This paper proposes a digital twin system based on microservices for satellite Internet emulation,namely Plotinus,which aims to solve these problems. Plotinus features a modular design, allowing for easy replacement of the physical layer to emulate different aerial vehicles and analyze channel interference. It also enables replacing of path computation methods to simplify testing and deploying algorithms. In particular, Plotinus allows for real-time emulation with live network traffic,enhancing practical network models. The evaluation result shows Plotinus’s effective emulation of dynamic satellite networks with real-world devices. Its adaptability for various communication models and algorithm testing highlights Plotinus’s role as a vital tool for developing and analyzing SAGIN systems, offering a cross-layer,real-time,and scalable digital twin system.

关键词： network emulation framework dynamic topology adjustment microservice architecture

Improvement in hydrogen storage performance of MgH2 by vanadium doped with ZIF-8 derived a single-atom catalyst V-N-C

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Rare Metals 2024年第6期43卷 2623-2635页

作者： Fei-Fan Hong Wei-Tao Shi Ruo-Lin Zhao Yi Fan Zi-Qi Liu Si-Zhi Ding Hai-Zhen Liu Wen-Zheng Zhou Jin Guo Zhi-Qiang Lan Guangxi Novel Battery Materials research center of Engineering Technology and center on Nanoenergy research Guangxi Colleges and Universities Key Laboratory of Blue Energy and Systems IntegrationCarbon Peak and Neutrality Science and Technology Development InstituteSchool of Physical Science and TechnologyGuangxi University Nanning Engineering Technology research center for Power Transmission system of New Energy Vehicle Transportation CollegeNanning University

Herein,the successful preparation of a singleatom catalyst V-N-C using vanadium-doped zeolitic imidazolate framework（ZIF）-8 as a precursor is reported.The experimental results showed that the V-N-C had a good promoting effect on the hydrogen storage performance of MgH2,and the optimal addition amount of V-N-C was 10wt%.The hydrogenation and dehydrogenation apparent activation energies of 10 wt% V-N-C-catalyzed MgH2were reduced by 44.9 and 53.5 kJ·mol-1,respectively,compared to those of additive-free MgH2.The 10 wt% V-N-C-catalyzed MgH2could reabsorb 5.92 wt% of hydrogen in 50 min at 150℃,with a capacity retention rate of 99.1% after 30 cycles of hydrogen absorption and desorption.Mechanism analysis showed that V-N-C was partially transformed into VN and metallic V when it was milled with MgH2;the in-situ-formed VN and metallic V played an important role in improving the hydrogen storage performance of MgH2.This approach provides a potential solution for obtaining high-performance Mg-based hydrogen storage materials through synergistic interactions between V,N and C.

关键词： MgH 2 Hydrogen storage materials Catalyst Hydrogen storage perormance

Energy-Efficient Driving Strategy for High-Speed Trains with Considering the Checkpoints

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Chinese Journal of Electronics 2024年第3期33卷 847-858页

作者： Zixuan ZHANG Yuan CAO Shuai SU State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University School of Electronic and Information Engineering Beijing Jiaotong University National Engineering research center of Rail transportation Operation and Control system Beijing Jiaotong University Frontiers Science center for Smart High-speed Railway system Beijing Jiaotong University

With rising energy prices and concerns about environmental issues,energy-efficient driving strategies(EEDS) for high-speed trains have received a substantial amount of attention.In particular,energy-saving schemes play a huge role in reducing the energy and operating costs of trains.This article studies the EEDS of high-speed trains at a given time.A well-posed model is formulated,in which the constraints of the checkpoints,in addition to the speed limits,vehicle dynamics,and discrete control throttle,are first considered.For a given control sequence,the Karush-Kuhn-Tucker(KKT) conditions are used to obtain the necessary conditions for an EEDS.According to several key equations of the necessary conditions,the checkpoint constraints are satisfied.Some case studies are conducted based on the data of the Beijing-Shanghai high-speed line to illustrate the effectiveness of the proposed approach.

关键词： Energy consumption Costs Simulation Mathematical models Energy efficiency Vehicle dynamics Optimization

Optimal Stochastic Scheduling Strategy of Multi-vector Energy Complex Integrated with Full-blown Power-to-biomethane Model

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Journal of Modern Power systems and Clean Energy 2024年第3期12卷 863-873页

作者： Guanwei Zeng Chengxi Liu Minfang Liao Yongjian Luo Xuzhu Dong Hubei Engineering and Technology research center for AC/DC intelligent Distribution Network the School of Electrical Engineering and Automation Wuhan University

We propose an optimal stochastic scheduling strategy for a multi-vector energy complex(MEC), considering a fullblown model of the power-to-biomethane(Pt M) process. Unlike conventional optimization that uses a simple efficiency coefficient to coarsely model energy conversion between electricity and biomethane, a detailed Pt M model is introduced to emphasize the reactor kinetics and chemical equilibria of methanation. This model crystallizes the interactions between the Pt M process and MEC flexibility, allowing to adjust the operating condition of the methanation reactor for optimal MEC operation in stochastic scenarios. Temperature optimization and flowsheet design of the Pt M process increase the average selectivity of methane(i.e., ratio between net biomethane production and hydrogen consumption) up to 83.7% in the proposed synthesis flowsheet. Simulation results can provide information and predictions to operators about the optimal operating conditions of a Pt M unit while improving the MEC flexibility.

关键词： Biological system modeling Hydrogen Resistance heating Stochastic processes Production Methane Inductors