Adaptive and ADRC information fusion method for high speed train braking 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.

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A SVM framework for fault detection of the braking system in a high speed train

Mechanical Systems and Signal Processing ◽

10.1016/j.ymssp.2016.10.034 ◽

2017 ◽

Vol 87 ◽

pp. 401-409 ◽

Cited By ~ 34

Author(s):

Jie Liu ◽

Yan-Fu Li ◽

Enrico Zio

Keyword(s):

Fault Detection ◽

High Speed ◽

High Speed Train ◽

Braking System

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Latent Leakage Fault Identification and Diagnosis Based on Multi-Source Information Fusion Method for Key Pneumatic Units in Chinese Standard Electric Multiple Units (EMU) Braking System

Applied Sciences ◽

10.3390/app9020300 ◽

2019 ◽

Vol 9 (2) ◽

pp. 300

Author(s):

Jianyong Zuo ◽

Jingxian Ding ◽

Furen Feng

Keyword(s):

Support Vector Machine ◽

Information Fusion ◽

Small Sample ◽

Support Vector ◽

Ratio Test ◽

Fusion Method ◽

Source Information ◽

Braking System ◽

Chinese Standard ◽

Multiple Units

To identify and diagnose the latent leakage faults of key pneumatic units in the Chinese standard Electric Multiple Units (EMU) braking system, a multi-source information fusion method based on Kalman filtering, sequential probability ratio test (SPRT), and support vector machine (SVM) is proposed. The relay valve is taken as an example for research. Firstly, Kalman's state estimation function is used to obtain the innovation sequence, and the innovation sequence is input into the SPRT model to help recognize latent leakage faults of the relay valve. Using this method, the problem of the incomplete training set of the traditional SPRT method due to the change of the braking level and the vehicle load is solved. Secondly, the eight time-domain parameters of the relay valve input and the output pressure signal are extracted as fault characteristics, and then input to the support vector machine to realize the internal and external leakage fault diagnosis of the relay valve, which provides a reference for maintenance. Finally, this method is verified by the fault simulation data by quickly identifying latent leakage faults and diagnosing the internal and external leakage at a fault recognition rate of 100% by SVM under small sample conditions.

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Innovative and Efficient Electric Braking System in High-Speed Trains using Proportional Resonant Controller

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b7551.129219 ◽

2020 ◽

Vol 9 (2) ◽

pp. 1691-1698

Keyword(s):

High Speed ◽

System Stability ◽

System Control ◽

African Buffalo ◽

High Speed Train ◽

Control Techniques ◽

Braking System ◽

High Speed Trains ◽

Electric Braking ◽

Electric Brake

The high-speed trains are eight times more efficient than traditional trains because it significantly operates faster than the other trains; however, the train accidents are happened as because of its poor braking system. From this reason, effective braking system control techniques are developed. In this paper, the electric brake regenerative system is introduced to control the high-speed train. Therefore the braking system of a high-speed train is modeled in Brushless Direct Current (BLDC) motor, which is controlled by the gain of Proportional Resonant (PR) controller. Subsequently, the parameters of the controller and error percentage from the controller in the braking system are optimized using Multi-Objective African Buffalo Optimization (MOABO). The developed controller in braking system stability is analyzing by the Lyapunov function. The results of the braking system are validating by the torque and speed of the high-speed train braking system. Furthermore, the proposed high-speed braking system control is compared with existing control techniques in a high-speed train.

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Fault Diagnosis for High-Speed Train Axle-Box Bearing Using Simplified Shallow Information Fusion Convolutional Neural Network

Sensors ◽

10.3390/s20174930 ◽

2020 ◽

Vol 20 (17) ◽

pp. 4930 ◽

Cited By ~ 1

Author(s):

Honglin Luo ◽

Lin Bo ◽

Chang Peng ◽

Dongming Hou

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Information Fusion ◽

High Speed ◽

High Reliability ◽

Operating Conditions ◽

High Speed Train ◽

Bearing Fault ◽

Bearing Fault Diagnosis

Axle-box bearings are one of the most critical mechanical components of the high-speed train. Vibration signals collected from axle-box bearings are usually nonlinear and nonstationary, caused by the complicated operating conditions. Due to the high reliability and real-time requirement of axle-box bearing fault diagnosis for high-speed trains, the accuracy and efficiency of the bearing fault diagnosis method based on deep learning needs to be enhanced. To identify the axle-box bearing fault accurately and quickly, a novel approach is proposed in this paper using a simplified shallow information fusion-convolutional neural network (SSIF-CNN). Firstly, the time domain and frequency domain features were extracted from the training samples and testing samples before been inputted into the SSIF-CNN model. Secondly, the feature maps obtained from each hidden layer were transformed into a corresponding feature sequence by the global convolution operation. Finally, those feature sequences obtained from different layers were concatenated into one-dimensional as the fully connected layer to achieve the fault identification task. The experimental results showed that the SSIF-CNN effectively compressed the training time and improved the fault diagnosis accuracy compared with a general CNN.

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