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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 *** fault diagnosis for railway point machines becomes a hot *** 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 ***,a two-stage feature selection method based on Fisher discrimination and ReliefF is proposed,which is validated more powerful than single feature selection ***,support vector machine is utilized for fault *** comparisons show that the proposed method performs *** diagnosis accuracies of normal-reverse and reverse-normal switching processes reach 100% and 96.57% ***,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 Railway point machine Vibration signal Variational mode decomposition Two-stage feature selection

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Effects of quantum noise on teleportation of arbitrary two-qubit state via five-particle Brown state

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Chinese Physics B 2024年第8期33卷 204-211页

作者：汪澳魏玉震姜敏李泳成陈虹黄旭 School of Electronics&Information Engineering Soochow UniversitySuzhou 215006China Key Laboratory of System Control and Information Processing Ministry of EducationShanghai 200240China School of Information Engineering Huzhou UniversityHuzhou 313000China

We propose a new protocol for quantum teleportation(QT)which adopts the Brown state as the quantum *** work focuses on the teleportation of a single unknown two-qubit state via a Brown state channel in an ideal *** validate the effectiveness of our proposed scheme,we conduct experiments by using the quantum circuit simulator ***,we investigate the effects of four noisy channels,namely,the phase damping noise,the bit-flip noise,the amplitude damping noise,and the phase-flip ***,we employ Monte Carlo simulation to elucidate the fidelity density under various noise *** analysis demonstrates that the fidelity of the protocol in a noisy environment is influenced significantly by the amplitude of the initial state and the noise factor.

关键词： quantum communication Brown state fidelity

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Generating High-Temperature Polymer Dielectrics with a Graph Convolution and Reinforcement Learning Framework

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Journal of Physical Chemistry C 2025年第22期129卷 10020-10026页

作者： Liu, Di-Fan Dong, Wen-Zhuo Wang, Xin-Jie Zhong, Shao-Long Dang, Zhi-Min State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Tsinghua University Beijing100084 China

Generating molecular structures with certain desired properties is a fundamental problem for material science research. Deep learning models have demonstrated big potential to identify novel materials by exploring large chemical spaces efficiently. Here, we demonstrate the generation of hypothetical polymers with a high glass-transition temperature (Tg) and a wide band gap (Eg) using a general graph convolutional network-based model through reinforcement learning. Two forward property prediction models on Tg and Eg are employed to provide domain-specific rewards. We further incorporate prior knowledge in the form of adversarial rewards given by a generative adversarial network framework via a data set of exemplar polymer structures. The domain-specific rewards and adversarial rewards are optimized in a reinforcement learning framework through policy gradient methods. Rules of chemistry are incorporated in the molecule generation environment as hard constraints. The generation process is guided to generate promising high-temperature polymer dielectrics for energy storage applications by property targeting and constrained property optimization. This work demonstrates potential for exploring large chemical spaces for the discovery of polymers with desirable properties. © 2025 American Chemical Society.

关键词： Deep reinforcement learning

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Parallel system Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective control

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CSEE Journal of Power and Energy systems 2024年第1期10卷 13-28页

作者： Tianyun Zhang Jun Zhang Feiyue Wang Peidong Xu Tianlu Gao Haoran Zhang Ruiqi Si School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072HubeiChina State Key Laboratory for Management and Control of Complex System Institute of AutomationChinese Academy of SciencesBeijing 100190China

In artificial intelligence(AI)based-complex power system management and control technology,one of the urgent tasks is to evaluate AI intelligence and invent a way of autonomous intelligence ***,there is,currently,nearly no standard technical framework for objective and quantitative intelligence *** this article,based on a parallel system framework,a method is established to objectively and quantitatively assess the intelligence level of an AI agent for active power corrective control of modern power systems,by resorting to human intelligence evaluation *** this basis,this article puts forward an AI self-evolution method based on intelligence assessment through embedding a quantitative intelligence assessment method into automated reinforcement learning(AutoRL)systems.A parallel system based quantitative assessment and self-evolution(PLASE)system for power grid corrective control AI is thereby constructed,taking Bayesian Optimization as the measure of AI evolution to fulfill autonomous evolution of AI under guidance of their intelligence assessment *** results exemplified in the power grid corrective control AI agent show the PLASE system can reliably and quantitatively assess the intelligence level of the power grid corrective control agent,and it could promote evolution of the power grid corrective control agent under guidance of intelligence assessment results,effectively,as well as intuitively improving its intelligence level through selfevolution.

关键词： AI quantitative intelligence assessment and self-evolution automated reinforcement learning Bayesian optimization corrective control parallel system

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Data-driven Anomaly Detection Method Based on Similarities of Multiple Wind Turbines

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Journal of Modern Power systems and Clean Energy 2024年第3期12卷 803-818页

作者： Xiangjun Zeng Ming Yang Chen Feng Mingqiang Wang Lingqin Xia the College of Electrical Engineering&New Energy China Three Gorges UniversityYichang 443002China the Key Laboratory of Power System Intelligent Dispatch and Control Shandong UniversityJinan 250061China

The operating conditions of wind turbines(WTs)in the same wind farm(WF)may share similarities due to their shared manufacturing process,control strategy,and operating ***,the similarities of WTs are seldom considered in WT anomaly detection,resulting in the disregard of useful *** paper proposes a method to improve the reliability and accuracy of WT anomaly detection using the supervisory control and data acquisition(SCADA)data of multiple WTs in the same ***,a similarity assessment method based on a comparison of different observation time series is proposed,which objectively quantifies the similarities of WT operating ***,the SCADA data of the target WT and selected WTs that are similar are used to establish several estimation models through a long short-term memory(LSTM)*** models that exhibit good estimation performance are used to construct a combined estimation model that estimates the variations in the monitored variables of the target ***,an anomaly detection method that jointly compares the effective value and information entropy of the residuals is proposed to identify *** effectiveness and accuracy of the proposed method are verified using the data of two actual WFs.

关键词： Anomaly detection information entropy long short-term memory similarity assessment wind farm wind turbines

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A Review on the Mullins Effect in Tough Elastomers and Gels

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Acta Mechanica Solida Sinica 2024年第2期37卷 181-214页

作者： Lin Zhan Shaoxing Qu Rui Xiao MOE Key Lab of Disaster Forecast and Control in Engineering College of Mechanics and Construction EngineeringJinan UniversityGuangzhou510632China State Key Laboratory of Fluid Power and Mechatronic System Key Laboratory of Soft Machines and Smart Devices of Zhejiang ProvinceDepartment of Engineering MechanicsZhejiang UniversityHangzhou310027China

Tough elastomers and gels have garnered broad research interest due to their wide-ranging potential ***,during the loading and unloading cycles,a clear stress softening behavior can be observed in many material systems,which is also named as the Mullins *** this work,we aim to provide a complete review of the Mullins effect in soft yet tough materials,specifically focusing on nanocomposite gels,double-network hydrogels,and multi-network *** first revisit the experimental observations for these soft *** then discuss the recent developments of constitutive models,emphasizing novel developments in the damage mechanisms or network *** phenomenological models will also be briefly *** attention is then placed on the anisotropic and multiaxial modeling *** is demonstrated that most of the existing models fail to accurately predict the multiaxial data,posing a significant challenge for developing future anisotropic models tailored for tough gels and elastomers.

关键词： Mullins effect Double-network hydrogels Nanocomposite gels Multi-network elastomers Anisotropic damage model Multiaxial model

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controllability of heterogeneous networked sampled-data systems

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Science China(Information Sciences) 2025年第3期68卷 302-318页

作者： Zixuan YANG Lin WANG Xiaofan WANG Guanrong CHEN Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China School of Mechatronic Engineering and Automation Shanghai University School of Electrical and Electronic Engineering Shanghai Institute of Technology Department of Electrical Engineering City University of Hong Kong

This study investigates the controllability of a general heterogeneous networked sampled-data system(HNSS) consisting of nonidentical node systems, where the inner coupling between any pair of nodes can be described by a unique *** signals on control and transmission channels are sampled and held by zero-order holders, and the control sampling period of each node can be different. Necessary and sufficient controllability conditions are developed for the general HNSS, using the Smith normal form and matrix equations, respectively. The HNSS in specific topology or dynamic settings is discussed subsequently with easier-to-verify conditions derived. These heterogeneous factors have been determined to independently or jointly affect the controllability of networked sampled-data systems. Notably, heterogeneous sampling periods have the potential to enhance the overall controllability, but not for systems with some special dynamics. When the node dynamics are heterogeneous,the overall system can be controllable even if it is topologically uncontrollable. In addition, in several typical heterogeneous sampled-data multi-agent systems, pathological sampling of single-node systems will necessarily cause overall uncontrollability.

关键词： network controllability sampled-data system heterogeneous system inner coupling multi-agent system

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Interval analysis for neural networks with application to fault detection

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Science China(Information Sciences) 2024年第11期67卷 222-237页

作者： Zhenhua WANG Youdao MA Song ZHU Thach Ngoc DINH Yi SHEN Department of Control Science and Engineering Harbin Institute of Technology National Key Laboratory of Complex System Control and Intelligent Agent Cooperation School of Mathematics China University of Mining and Technology The CEDRIC-Lab Conservatoire National des Arts et Métiers (CNAM)

This paper investigates an interval analysis method for neural networks and applies it to fault detection for systems with unknown but bounded measurement noise. First, a novel interval analysis method is presented, which can compute the bounds of the output of a feedforward neural network subject to a bounded input. By applying the proposed interval analysis method to a network trained with fault-free system data, adaptive thresholds for fault detection are computed. Finally, one can acquire fault detection results via a fault detection strategy. The proposed method can achieve tight bounds of the network output and employ simple operations, which leads to accurate fault detection results and a low computational burden.A numerical simulation and an experiment on an AC servo motor are given to illustrate the effectiveness and superiority of the proposed method.

关键词： interval analysis feedforward neural network fault detection adaptive thresholds

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Cognitive Navigation for Intelligent Mobile Robots:A Learning-Based Approach With Topological Memory Configuration

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IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 1933-1943页

作者： Qiming Liu Xinru Cui Zhe Liu Hesheng Wang Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China MoE Key Laboratory of Artificial Intelligence AI InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of EducationKey Laboratory of Marine Intelligent Equipment and System of Ministry of EducationShanghai Engineering Research Center of Intelligent Control and ManagementShanghai Jiao Tong UniversityShanghai 200240China

Autonomous navigation for intelligent mobile robots has gained significant attention,with a focus on enabling robots to generate reliable policies based on maintenance of spatial *** this paper,we propose a learning-based visual navigation pipeline that uses topological maps as memory *** introduce a unique online topology construction approach that fuses odometry pose estimation and perceptual similarity *** tackles the issues of topological node redundancy and incorrect edge connections,which stem from the distribution gap between the spatial and perceptual ***,we propose a differentiable graph extraction structure,the topology multi-factor transformer(TMFT).This structure utilizes graph neural networks to integrate global memory and incorporates a multi-factor attention mechanism to underscore elements closely related to relevant target cues for policy *** from photorealistic simulations on image-goal navigation tasks highlight the superior navigation performance of our proposed pipeline compared to existing memory *** validation through behavior visualization,interpretability tests,and real-world deployment further underscore the adapt-ability and efficacy of our method.

关键词： Graph neural networks(GNNs) spatial memory topological map visual navigation

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Optimal Configuration Planning of Multi-energy Microgrid Based on Source-load-temperature Scenarios Deep Joint Generation

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CSEE Journal of Power and Energy systems 2023年第3期9卷 1090-1102页

作者： Nantian Huang Wenting Wang Guowei Cai School of Electrical Engineering Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology(Northeast Electric Power University)Jilin 132012China

An optimal configuration method of a multi-energy microgrid system based on the deep joint generation of sourceload-temperature scenarios is proposed to improve the multienergy complementation and the reliability of energy supply in extreme ***,based on the historical meteorological data,the typical meteorological clusters and extreme temperature types are ***,to reflect the uncertainty of energy consumption and renewable energy output in different weather types,a deep joint generation model using a radiation-electric load-temperature scenario based on a denoising variational autoencoder is established for each weather *** the same time,to cover the potential high energy consumption scenarios with extreme temperatures,the extreme scenarios with fewer data samples are ***,the scenarios are reduced by clustering *** normal days of different typical scenarios and extreme temperature scenarios are determined,and the cooling and heating loads are determined by ***,the optimal configuration of a multi-energy microgrid system is carried *** show that the optimal configuration based on the extreme scenarios and typical scenarios can improve the power supply reliability of the *** proposed method can accurately capture the complementary potential of energy *** the economy of the system configuration is improved by 14.56%.

关键词： Deep joint generation denoising variational autoencoders multi-energy microgrid system optimal configuration planning

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