Arc Detection and Recognition in the Pantograph-Catenary System Based on Multi-Information Fusion

2020 ◽  
Vol 2674 (10) ◽  
pp. 229-240
Author(s):  
Shize Huang ◽  
Wei Chen ◽  
Bo Sun ◽  
Ting Tao ◽  
Lingyu Yang
Keyword(s):  
Visible Light ◽  
Information Fusion ◽  
High Speed ◽  
Evidence Theory ◽  
Detection Methods ◽  
Fusion Method ◽  
Fusion Model ◽  
Threshold Method ◽  
High Speed Trains ◽  
Catenary System

The pantograph-catenary system is critical to high-speed railways. Electric arcs in the pantograph-catenary system indicate possible damages to the whole railway system, and detecting them in time has been a critical task. In this paper, a fusion method for the pantograph-catenary arc detection based on multi-type videos is proposed. First, convolutional neural network (CNN) is employed to detect arcs in visible light images, and a threshold method is applied to identify arcs in infrared images. Second, the CNN-based environment perception model is established on visible light images. It obtains the dynamical adjustment of the reliability weights for different scenarios where trains usually work. Finally, the information fusion model based on evidence theory uses those weights and integrates the detection results on visible light images and infrared results. The experimental results demonstrate the fusion method can avoid misjudgments of the two individual detection methods in certain scenarios, and perform better than each of them. This approach can adapt to the complex environments of high-speed trains.

Fault Classification of Hydroelectric Generating Unit Based on Improved Evidence Theory

2014 ◽  
Vol 7 (1) ◽  
pp. 78-83 ◽  
Author(s):  
Jiatang Cheng ◽  
Li Ai ◽  
Zhimei Duan ◽  
Yan Xiong
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Evidence Theory ◽  
Fault Classification ◽  
Fusion Method ◽  
Inconsistent Result ◽  
High Conflict

Aiming at the problem of the conventional vibration fault diagnosis technology with inconsistent result of a hydroelectric generating unit, an information fusion method was proposed based on the improved evidence theory. In this algorithm, the original evidence was amended by the credibility factor, and then the synthesis rule of standard evidence theory was utilized to carry out information fusion. The results show that the proposed method can obtain any definitive conclusion even if there is high conflict evidence in the synthesis evidence process, and may avoid the divergent phenomenon when the consistent evidence is fused, and is suitable for the fault classification of hydroelectric generating unit.

A novel information fusion method based on Dempster-Shafer evidence theory for conflict resolution

2011 ◽  
Vol 15 (3) ◽  
pp. 399-411 ◽  
Author(s):  
Jianping Yang ◽  
Hong-Zhong Huang ◽  
Qiang Miao ◽  
Rui Sun
Keyword(s):  
Conflict Resolution ◽  
Information Fusion ◽  
Evidence Theory ◽  
Fusion Method

A Novel Information Fusion Method of the Infrared and Visible Light Images Based on Target Region Feature

Applied laser ◽  
2012 ◽  
Vol 32 (5) ◽  
pp. 440-445
Author(s):  
钟平 Zhong Ping ◽  
施云龙 Shi Yunlong ◽  
杨晓冬 Yang Xiaodong ◽  
王洋 Wang Yang ◽  
王士乐 Wang Shile
Keyword(s):  
Visible Light ◽  
Information Fusion ◽  
Fusion Method ◽  
Target Region ◽  
Region Feature

scholarly journals Application of Two-Level Joint Information Fusion Model in Intelligent Vehicle

2016 ◽  
Vol 12 (03) ◽  
pp. 77
Author(s):  
Yan Ting Lan ◽  
Jiinying Huang ◽  
Xiaodong Chen
Keyword(s):  
Information Fusion ◽  
Feature Fusion ◽  
Target Identification ◽  
Information Source ◽  
Evidence Theory ◽  
Intelligent Vehicles ◽  
Fusion Model ◽  
Multiple Features ◽  
Model Combining ◽  
Real Vehicle

This paper proposes a two-level joint information fusion model combining BP neural network and D-S evidence theory. The model of great practical value reduces target identification error probability by multiple features of the target information, shows good scalability with its two steps of information fusion model, and conveniently increases/reduces feature fusion information source according to different situations and different objects. The method used for intelligent vehicles has good flexibility and robustness in tracking and avoiding obstacle. The simulation and real vehicle tests have verified effectiveness of the method.

scholarly journals Active Control of Contact Force for a Pantograph-Catenary System

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Jiqiang Wang
Keyword(s):  
Active Control ◽  
Contact Force ◽  
High Speed ◽  
Large Scale ◽  
Vibration Suppression ◽  
Numerical Study ◽  
Primary Objective ◽  
Geometric Framework ◽  
High Speed Trains ◽  
Catenary System

The performance of the high speed trains depends critically on the quality of the contact in the pantograph-catenary interaction. Maintaining a constant contact force needs taking special measures and one of the methods is to utilize active control to optimize the contact force. A number of active control methods have been proposed in the past decade. However, the primary objective of these methods has been to reduce the variation of the contact force in the pantograph-catenary system, ignoring the effects of locomotive vibrations on pantograph-catenary dynamics. Motivated by the problems in active control of vibration in large scale structures, the author has developed a geometric framework specifically targeting the remote vibration suppression problem based only on local control action. It is the intention of the paper to demonstrate its potential in the active control of the pantograph-catenary interaction, aiming to minimize the variation of the contact force while simultaneously suppressing the vibration disturbance from the train. A numerical study is provided through the application to a simplified pantograph-catenary model.

Research on Evaluation of Vehicle Dangerous Traveling State Based on Information Fusion Method

2013 ◽  
Vol 791-793 ◽  
pp. 1018-1022
Author(s):  
Peng Wang ◽  
Zhi Qiang Liu
Keyword(s):  
Information Fusion ◽  
Evaluation System ◽  
Evidence Theory ◽  
Recognition System ◽  
Fusion Method ◽  
Source Information ◽  
State Recognition ◽  
Research On Evaluation ◽  
Lane Departure ◽  
Level Information

An evaluation system of vehicle traveling state was proposed,and an unsafe vehicle traveling state recognition system was established using multi-level information fusion method. In view of the effects of the complexity of the driving environment, a variety of working conditions and the diversity of vehicle traveling characteristics, combing BP neural network with Dempster-Shafer evidence theory technique, the multi-information decision-level fusion was proposed to estimate the different kind model of the vehicle status. To verify the proposed strategies,the vehicle traveling posture evaluation system was established. The lane departure parameters and the relative distance parameters were studied in order to get the characterization of the vehicle traveling status information. The simulation results indicate that the adaptability and accuracy and the intelligence level of driving characterization estimation are significantly improved by using the pattern classification and decision technology of multi-source information fusion.

scholarly journals Sensors Information Fusion System with Fault Detection Based on Multi-Manifold Regularization Neighborhood Preserving Embedding

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1440
Author(s):  
Jianping Wu ◽  
Bin Jiang ◽  
Hongtian Chen ◽  
Jianwei Liu
Keyword(s):  
Fault Detection ◽  
Information Fusion ◽  
High Speed ◽  
Euclidean Distance ◽  
Manifold Regularization ◽  
Fusion System ◽  
Electrical Drive ◽  
High Speed Trains ◽  
Sensor Information ◽  
Drive Systems

Electrical drive systems play an increasingly important role in high-speed trains. The whole system is equipped with sensors that support complicated information fusion, which means the performance around this system ought to be monitored especially during incipient changes. In such situation, it is crucial to distinguish faulty state from observed normal state because of the dire consequences closed-loop faults might bring. In this research, an optimal neighborhood preserving embedding (NPE) method called multi-manifold regularization NPE (MMRNPE) is proposed to detect various faults in an electrical drive sensor information fusion system. By taking locality preserving embedding into account, the proposed methodology extends the united application of Euclidean distance of both designated points and paired points, which guarantees the access to both local and global sensor information. Meanwhile, this structure fuses several manifolds to extract their own features. In addition, parameters are allocated in diverse manifolds to seek an optimal combination of manifolds while entropy of information with parameters is also selected to avoid the overweight of single manifold. Moreover, an experimental test based on the platform was built to validate the MMRNPE approach and demonstrate the effectiveness of the fault detection. Results and observations show that the proposed MMRNPE offers a better fault detection representation in comparison with NPE.

scholarly journals An Algorithm of Wireless Sensor Monitoring System

2018 ◽  
Vol 14 (01) ◽  
pp. 52
Author(s):  
Li Hongri
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Monitoring System ◽  
Information Fusion ◽  
Design Method ◽  
Storage System ◽  
Wireless Sensor ◽  
Fusion Method ◽  
Fusion Model ◽  
Accurate Identification

In order to realize more intelligent storage monitoring system, the information fusion model of wireless sensor network for storage environment monitoring is studied on the basis of analyzing information fusion technology. By analyzing the structure of storage monitoring system based on wireless sensor network, a two-layer information fusion method is established. The information fusion of homogeneous sensor based on adaptive weighting and the fusion method of heterogeneous sensor based on radial basis function neural network are designed and verified. The experimental results show that the design method can fuse the storage environment information and realize the accurate identification of the environmental state. Therefore, the algorithm can effectively improve the speed of network training, and the classification effect is good. To a certain extent, it can help enterprises to establish a safe and efficient storage system, to enhance the efficiency of enterprise warehousing operations.

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