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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 Vibrations Support vector machines Diversity reception Switches Feature extraction Rail transportation

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 2024年

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%.

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An improved YOLOv7 for the state identification of sliding chairs in railway turnout

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High-Speed Railway 2024年第2期2卷 71-76页

作者： Yuan Cao Zongbao Liu Feng Wang Shuai Su yongkui sun Wenkun Wang National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong UniversityBeijing 100044China School of Automation and Intelligence Beijing Jiaotong UniversityBeijing 100044China Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong UniversityBeijing 100044China Zhuzhou CRRC Times Electric Co. LTD.Zhuzhou 412001China

The sliding chairs are important components that support the switch rail conversion in the railway *** to the harsh environmental erosion and the attack from the wheel vibration,the failure rate of the sliding chairs accounts for up to 10%of the total failure number in ***,there is little research carried out in the existing literature to diagnose the deterioration states of the sliding *** fill out this gap,by utilizing the images containing the sliding chairs,we propose an improved You Only Look Once version 7(YOLOv7)to identify the state of the sliding ***,to meet the challenge brought by the small inter-class differences among the sliding chair states,we first integrate the Convolutional Block Attention Module(CBAM)into the YOLOv7 backbone to screen the information conducive to state ***,an extra detector for a small object is customized into the YOLOv7 network in order to detect the small-scale sliding chairs in ***,we revise the localization loss in the objective function as the Efficient Intersection over Union(EIoU)to optimize the design of the aspect ratio,which helps the localization of the sliding ***,to address the issue caused by the varying scales of the sliding chairs,we employ K-means++to optimize the priori selection of the initial anchor ***,based on the images collected from real-world turnouts,the proposed method is verified and the results show that our method outperforms the basic YOLOv7 in the state identification of the sliding chairs with 4%improvements in terms of both mean Average Precision@0.5(mAP@0.5)and F1-score.

关键词： Railway turnout Sliding chairs State identification Object detection

Condition Monitoring for Railway Point Machines Based on Sound Analysis and Support Vector Machine

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Chinese Journal of Electronics 2020年第4期29卷 786-792页

作者： sun yongkui CAO Yuan XIE Guo WEN Tao School of Electronic and Information Engineering Beijing Jiaotong University National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology

Railway point machines(RPMS) are one of the key equipments in the railway system to switch different routes for the *** monitoring for RPMs is a vital measure to keep train operation safe and *** convenience and low cost into consideration, a novel intelligent condition monitoring method for RPMs based on sound analysis is ***-domain and frequency-domain features are obtained,and normalized using z-score standardization method to eliminate the influences of different *** particle swarm optimization(BPSO) is utilized to select the most significant discrimination feature *** effects of the selected optimal features are verified using Support vector machine(SVM), 1-Nearest neighbor(1 NN), Random forest(RF), and Naive Bayes(NB).Experiment results indicate SVM performs best on identification accuracy and computing cost compared with the other three *** identification accuracies on normal switching and reverse switching processes reach100% and 99.67%, respectively, indicating the feasibility of the proposed method.

关键词： Condition monitoring Sound analysis Railway point machines(RPMs) Binary particle swarm optimization(BPSO) Support vector machine(SVM)

Research on head way control of a guided transport system based on intersection condition evaluation

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Transportation Safety and Environment 2023年第4期5卷 29-35页

作者： Xiao Yu Yuan Cao yongkui sun Signal and Communication Research Institute China Academy of Railway Sciences Corporation LimitedBeijing 100081China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijing 100044China National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong UniversityBeijing 100044China

ln order to ensure the safety and efficiency of the section tracking operation of a guided transport system,a safety headway control method of section tracking based on intersection conditions is proposed in this *** the difference of signal phase,the evaluation model of road conditions was established based on a fuzzy comprehensive evaluation method,*** on the artificial potential field method,the time-varying hybrid artificial potential field(TH-APF)method was proposed,and the tracking headway control algorithm was designed to realize the dynamic control of the tracking headway of the guide transport *** simulation results verified the effectiveness and applicability of the evaluation model of intersection road conditions;the tracking headway can be maintained at about 120 *** tracking headway control algorithm of guided transport vehicles can respond to the road conditions and avoid the local optimum of the artificial potential field method,thus improving the operating efficiency.

关键词： guided transport system intersection condition evaluation artificial potential field method tracking headway control

Research on fault diagnosis of a railway point machine based on a multi-entropy feature extraction method and support vector machine

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Transportation Safety and Environment 2023年第4期5卷 58-66页

作者： Yunting Zheng Shaohua Chen Zhiyong Tan yongkui sun Automation and Electrical Engineering College Dalian Jiaotong UniversityDalian 116028LiaoningChina CRRC Dalian Co.Ltd. Dalian 116021LiaoningChina National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong UniversityBeijing 100044China

A new fault diagnosis method is proposed to effectively extract the fault features of the sound signal of typical faults of ZDJ9 railway point machines.A multi-entropy feature extraction method is proposed by combing multi-scale permutation entropy and wavelet packet ***,empirical mode decomposition is performed on sound signals to obtain modal components with different time ***,multi-scale permutation entropy is extracted from these ***,the wavelet packet entropy of the sound signals of these sensitive nodes is obtained by analysing the reconstructed signals of the last layer *** the multi-scale permutation entropy and the wavelet packet entropy can distinguish the subtle features of the signal,the subtle features of the information among the high-dimensional features,ReliefF is ***,a support vector machine(SVM)is used to judge the original sismal can be obtained as the feature vector of the 2DJ9 iway point mnchine in ditterent states,To reduce the redundant fault type of a ZDJ9 rilway point machine.

关键词： railway point machine fault diagnosis sound signal support vector machine(SVM)

Data-driven technology of fault diagnosis in railway point machines:review and challenges

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Transportation Safety and Environment 2022年第4期4卷 19-32页

作者： Xiaoxi Hu Yuan Cao Tao Tang yongkui sun State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijing 100044China School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China National Engineering Research Center of Rail Transportation Operation Control System Beijing Jiaotong UniversityBeijing 100044China.

Safety and reliability are absolutely vital for sophisticated Railway Point Machines(RPMs).Hence,various kinds of sensors and transducers are deployed on RPMs as much as possible to monitor their behaviour for detection of incipient faults and anticipation using data-driven *** paper firstly analyses and summarizes six RPMs’characteristics and then reviews the data-driven algorithms applied to fault diagnosis in RPMs during the past *** provides not only the process and evaluation metrics but also the pros and cons of these different ***,regarding the characteristics of RPMs and the existing studies,eight challenging problems and promising research directions are pointed out.

关键词： Railway point machine(RPM) fault diagnosis data-driven technology condition monitoring

Driving and tilt-hovering-An agile and manoeuvrable aerial vehicle with tiltable rotors

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IET Cyber-Systems and Robotics 2021年第2期3卷 103-115页

作者： Binbin Li Lei Ma Duo Wang yongkui sun School of Electrical Engineering Southwest Jiaotong UniversityChengduChina

Driving and tilt-hovering help unmanned aerial vehicles extend their time of operation and the visible range of the equipped *** quadrotors are underactuated,whose translational and rotational motions are strongly *** a result,it is impossible to maintain the attitude invariably while changing ***,larger flying speed results in greater air resistance,which is also not desirable.A novel aerial platform with tiltable rotors is *** with standard quadrotors,the aerial vehicle has three special capabilities:vector-flying,tilt-hovering,and *** platform consists of two tilting-axes to change the direction of thrusts with respect to the aircraft *** guarantee flexible manoeuvres,a geometric tracking control scheme that adapts on the fly to the thrust vectoring is *** of the proposed vehicle in different manoeuvres is demonstrated,such as flight with vectoring thrust that can reduce the flight resistance,tilt-hovering expanding the reachable set of the equipped *** prototype equipped with an electromechanical controller is constructed,and several associated preliminary experiments are *** feasibility of the mechanical design and the geometric control strategy are demonstrated.

关键词： vectoring thrust equipped

Condition monitoring and fault diagnosis strategy of railway point machines using vibration signals

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Transportation Safety and Environment 2023年第2期5卷 27-37页

作者： yongkui sun Yuan Cao Haitao Liu Weifeng Yang Shuai Su National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong UniversityBeijing 100044China CRRC Zhuzhou Institute Co Ltd Zhuzhou 412001HunanChina Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong UniversityBeijing 100044China

Condition monitoring of railway point machines is important for train operation safety and *** to the fields of mechanical equipment fault detection,this paper proposes a fault detection and identification strategy of railway point machines via vibration signals.A comprehensive feature distilling approach by combining variational mode decomposition(VMD)energy entropy and time-and frequency-domain statistical features is presented,which is more effective than single type of *** optimal set of features was selected with ReliefF,which helps improve the diagnosis *** vector machine(SVM),which is suitable for a small sample,is adopted to realize *** diagnosis accuracy of the proposed method reaches 100%,and its effectiveness is verified by experiment *** this paper,vibration signals are creatively adopted for fault diagnosis of railway point *** presented method can help guide field maintenance staff and also provide reference for fault diagnosis of other equipment.

关键词： railway point machines condition monitoring variational mode decomposition(VMD)energy entropy statistical features

Rail fastener detection of heavy railway based on deep learning

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High-Speed Railway 2023年第1期1卷 63-69页

作者： Yuan Cao Zihao Chen Tao Wen Clive Roberts yongkui sun Shuai Su National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong UniversityBeijing 100044China School of Electronic and Information Engineering Bejing Jiaotong UniversityBeijing 100044China Birmingham Centre for Railway Research and Education University of BirminghamBirmingham B152TTUK Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong UniversityBeijing 100044China

Image detection based on machine learning and deep learning currently has a good application prospect for railway fault diagnosis,with good performance in feature extraction and the accuracy of image localization and good classification *** improve the speed of locating small target objects of fasteners,the YOLOv5 framework model with faster algorithm speed is *** improve the classification accuracy of fasteners,YOLOv5-based heavy-duty railway rail fastener detection is *** anchor size is modified on the original basis to improve the attention to small targets of *** CBAM(Convolutional Block Attention Module)module and TPH(Transformer Prediction Head)module are introduced to improve the speed and accuracy *** rail fasteners are divided into 6 *** comparisons show that before the improvement,the MAP@0.5 value of all categories are close to the peak of 0.989 after the epoch of 150,and the F1 score approaches 1 with confidence in the interval(0.2,0.95).The improved mAP@0.5 value approached the highest value of 0.991 after the epoch of 75,and the F1 score approached 1 with confidence in the interval(0.01,0.95).The experiment results indicate that the improved YOLOv5 model proposed in this paper is more suitable for the task of detecting rail fasteners.

关键词： Rail fasteners Fault diagnosis Heavy haul railways Deep learning YOLO5