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Fault Diagnosis for Railway Point Machines Using VMD Multi-Scale Permutation Entropy and ReliefF Based on Vibration Signals

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Chinese Journal of Electronics 2025年第1期34卷 204-211页

作者： Yongkui Sun Yuan Cao Peng Li Shuai Su National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University School of Automation and Intelligence Beijing Jiaotong University Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University

The railway point machine plays an important part in railway systems. It is closely related to the safe operation of trains. Considering the advantages of vibration signals on anti-interference, this paper develops a novel vibration signal-based diagnosis approach for railway point machines. First, variational mode decomposition(VMD)is adopted for data preprocessing, which is verified more effective than empirical mode decomposition. Next, multiscale permutation entropy is extracted to characterize the fault features from multiple scales. Then ReliefF is utilized for feature selection, which can greatly decrease the feature dimension and improve the diagnosis accuracy. By experiment comparisons, the proposed approach performs best on diagnosis for railway point machines. The diagnosis accuracies on reverse-normal and normal-reverse processes are respectively 100% and 98.29%.

关键词： Vibrations Fault diagnosis Accuracy Data preprocessing Switches Feature extraction Rail transportation Entropy Maintenance Monitoring

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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

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Nanosize-effect on the distribution of heavy metals in copper smelting dust:Based on sophisticated dust sorting approach

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Nano research 2024年第1期17卷 312-320页

作者： Ken Li Zuwu Liao Hengdi Ye Qingzhu Li Fenghua Shen Qian Li Hui Liu Qingwei Wang Xu Yan Zhang Lin Liyuan Chai School of Metallurgy and Environment Central South UniversityChangsha 410083China Chinese National Engineering Research Center for Control&Treatment of Heavy Metal Pollution Changsha 410083China National Engineering Research Center of Sintering and Pelletizing Equipment System Zhongye Changtian International Engineering Co.LtdChangsha 410205China

Dust particles emitted from smelters can be hazardous to ecosystems and humans,as they are often enriched in metallic ***,we combined multi-method mineralogical analysis with a sophisticated size sorting approach for copper smelting dust to study the nanosize-effect on heavy metal distribution,which has hitherto been *** types of dust were collected from a copper flash smelter and then size-sorted using a Dekati low-pressure *** showed that all three samples could easily sort out nanoscale dust particles(<1μm,grades 10–2)and even those smaller than 100 nm(grades 5–2).Especially for electrostatic precipitators dust,the mass fraction of nanoscale dust(<1μm)could reach 10.71%.The presence of heavy metals(Pb,Zn,Cu,and As)and their mineral species in dust was examined at various particle *** was discovered that different heavy metals are enriched on nanoparticles in specific *** micron-sized particles,heavy metals are generally found in discrete phases(e.g.,CuSO_(4),PbSO_(4),and As_(2)O_(3)).In nanoscale particles,the dominant phase is Fe_(3)O_(4),while heavy metals are mostly found in lattice substitution(e.g.,CuFe_(2)O_(4)and ZnFe_(2)O_(4)).Two distinct nano-dust morphologies were found:One with irregular mesh or chain structures consisting of particles of a few nanometers,and the other with polygonal crystals in larger sizes of hundreds of *** enrichment of heavy metals in the latter morphology is more pronounced,possibly because lattice substitution of heavy metals is more likely to occur when polycrystalline particles are formed.

关键词： copper flue dust size-sorted heavy metal distribution morphology nano-sized effects

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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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A Fusion Modeling Method for Ball Screw Feed system of Machine Tool 4th

A Fusion Modeling Method for Ball Screw Feed System of Machi...

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4th International Conference on Mechanical Design and Simulation, MDS 2024

作者： Huang, Dehai Duan, Jihang Yang, Jianzhong Chen, Jihong Xu, Guangda National Numerical Control System Engineering Research Center Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789819778867

The feed system is an important part of the CNC machine tool, and its performance directly affects the machining quality. Establishing an accurate model is the key to achieving feed system servo performance optimization and error compensation. In this paper, a fusion modeling method is proposed for the problems of low accuracy and difficulty in identifying stiffness and damping in the traditional lumped mass modeling method. The proposed method first establishes a rigid model without stiffness and damping, then uses a data-driven model to model the unmodeled dynamics, and finally connects the rigid model and the data-driven model in series. The method simplifies the lumped mass model, avoids the problem that the stiffness and damping are difficult to identify, and the fusion of the data-driven model significantly improves the prediction accuracy of the model. The experimental results show that the fused model has higher prediction accuracy for the frequency response characteristics and displacement of the feed system. © The Author(s) 2025.

关键词： Ball screws

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Collision-Free Algorithm for Industrial Robots Based GJK and OBB Theories

Collision-Free Algorithm for Industrial Robots Based GJK and...

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International Conference on SmartRail, Traffic, and Transportation engineering, ICSTTE 2023

作者： Aalhasan, Ali Joodi Bao, Song National Engineering Research Center of Numerical Control System Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9789819736812

This paper proposes a better way for industrial robots to avoid collisions while manipulating objects. The method uses the Oriented Bounding Boxes (OBB) theory. OBB is effective because it can work with objects turned in any direction, which makes it very exact. To detect if two OBBs are colliding, the algorithm adopted double-check collision detection to achieve a collision-free workspace. First, Threshold distance collision detection by calculating the nearest line between an object’s corners. Then by the Gilbert-Johnson-Keerthi (GJK) algorithm. The algorithm uses Z-Y-Z Euler's angles to handle orientation between robot grippers and objects during paths. This updated algorithm will make industrial robots safer and more efficient when handling objects. We have adopted Coppellia Simulation software to verify the accuracy of the algorithm with different scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Industrial robots

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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 *** particular,energy-saving schemes play a huge role in reducing the energy and operating costs of *** 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 *** a given control sequence,the Karush-Kuhn-Tucker(KKT) conditions are used to obtain the necessary conditions for an *** to several key equations of the necessary conditions,the checkpoint constraints are *** 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-efficiency High-speed trains Checkpoints

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Vibration Signal-Based Fault Diagnosis of Railway Point Machines via Double-Scale CNN

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Chinese Journal of Electronics 2023年第5期32卷 972-981页

作者： CHEN Xiaohan HU Xiaoxi WEN Tao CAO Yuan The School of Electronic and Information Engineering Beijing Jiaotong University The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University The National Engineering Research Center of Rail Transportation Operation Control System Beijing Jiaotong University

In the railway transportation industry,fault diagnosis of railway point machines(RPMs) is *** operational vibration signals can reflect the condition of various faults in mechanical devices, vibration sensing and monitoring and more importantly, vibration signal-based fault diagnosis for RPMs have attracted the attention of scholars and engineers. Most vibration signalbased fault-diagnosis methods for RPMs rely on data collected using high-sampling-rate sensors and manual feature extraction, hence are costly and insufficiently *** overcome these shortcomings, we propose a doublescale wide first-layer kernel convolutional neural network(DS-WCNN) for RPMs fault diagnosis using inexpensive and low-sampling-rate vibration sensors. The proposed wide first-layer kernels, which extract features from vibration observations, are particularly suitable for lowsampling-rate signals. Meanwhile, the proposed doublescale structure improves accuracy and noise suppression by combining two types of timescale features. Sufficient experiments, including noise addition and comparison,were conducted to demonstrate the robustness and accuracy of the proposed algorithm.

关键词： Vibrations Fault diagnosis Transportation industry Feature extraction Rail transportation Robustness Convolutional neural networks

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research on Virtual Coupled Train control Method Based on GPC & VAPF

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Chinese Journal of Electronics 2022年第5期31卷 897-905页

作者： CAO Yuan YANG Yaran MA Lianchuan WEN Jiakun State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University School of Electronic and Information Engineering Beijing Jiaotong University

Improving transportation efficiency is an eternal research hotspot in rail transit system. In recent years, the train operation control method based on virtual coupling has attracted the attention of many *** method of train coordination and anti-collision control is not only the key to realize the virtual coupling of train, but also the key to ensure the safety of train operation. Therefore, based on the existing research, a virtual coupled train dynamics model with nonlinear dynamics is established. Then, the parameters of the operation process model of the nonlinear virtual coupled train are identified by the recursive least squares method based on realtime data, which is applied to the variable parameter artificial potential field(VAPF) for parameter identification.A fusion controller based on feature-based generalized model prediction(GPC) and VAPF is used to control the virtual coupled train and prevent collision. Finally, the validity of the proposed method is verified by using real high-speed railway data.

关键词： recursive least squares method transportation vehicle dynamics train operation control transportation efficiency anti-collision control train operation safety feature-based generalized model prediction railway safety nonlinear dynamical systems fusion controller collision avoidance operation process model nonlinear virtual coupled train parameter identification nonlinear control systems variable parameter artificial potential field parameter estimation least squares approximations rail transit system virtual coupled train control predictive control train coordination collision prevention VAPF virtual coupled train dynamics railways high-speed railway data nonlinear dynamics virtual coupling GPC

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Modified multi-scale symbolic dynamic entropy and fuzzy broad learning-based fast fault diagnosis of railway point machines

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Transportation Safety and Environment 2023年第4期5卷 1-7页

作者： Junqi Liu Tao Wen Guo Xie Yuan Cao Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of TechnologyXi’an 710048ShaanxiChina School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China National Engineering Research Center of Rail Transportation Operation Control System Beijing Jiaotong UniversityBeijing 100044China

Railway point machine(RPM)condition monitoring has attracted engineers’attention for safe train operation and accident *** realize the fast and accurate fault diagnosis of RPMs,this paper proposes a method based on entropy measurement and broad learning system(BLS).Firstly,the modified multi-scale symbolic dynamic entropy(MMSDE)module extracts dynamic characteristics from the collected acoustic signals as entropy ***,the fuzzy BLS takes the above entropy features as input to complete model *** BLS introduces the Takagi-Sug eno fuzzy system into BLS,which improves the model’s classification performance while considering computational *** results indicate that the proposed method significantly reduces the running time while maintaining high accuracy.

关键词： railway point machine(RPM) fault diagnosis modified multi-scale symbolic dynamic entropy(MMSDE) fuzzy board learning system(BLS)

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