scholarly journals An efficient anomaly detection for high-speed train braking system using broad learning system

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Chong Wang ◽  
Jie Liu
Keyword(s):  
Anomaly Detection ◽  
High Speed ◽  
Learning System ◽  
High Speed Train ◽  
Braking System

scholarly journals Adaptive and ADRC information fusion method for high speed train braking system

2021 ◽  
pp. 171248
Author(s):  
Xiaojun Ma ◽  
Yuhua Qin ◽  
Dequan Kong ◽  
Desheng Liu ◽  
Chaoyang Wang
Keyword(s):  
Information Fusion ◽  
High Speed ◽  
Fusion Method ◽  
High Speed Train ◽  
Braking System

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

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.

A novel abnormal detection method for bearing temperature based on spatiotemporal fusion

2021 ◽  
pp. 095440972110221
Author(s):  
Yong Zhi Liu ◽  
Yi Sheng Zou ◽  
Yu Wu ◽  
Hao Yang Zhang ◽  
Guo Fu Ding
Keyword(s):  
Decision Making ◽  
Anomaly Detection ◽  
Real Time ◽  
Early Warning ◽  
High Speed ◽  
Entropy Method ◽  
High Speed Train ◽  
Detection Model ◽  
Time Prediction ◽  
Model Based

The existing bearing temperature fault detection and early warning system has a high false alarm rate and insufficient early warning ability. For this reason, in this study, a method for detecting the abnormal bearing temperature of high-speed trains based on spatiotemporal fusion decision-making was proposed. First, the temperature characteristics of similar bearings were compared and analyzed with different spatial distributions. Then, a bearing abnormal temperature rise detection model based on the analytic hierarchy process (AHP) entropy method was proposed. Second, the temperature characteristics of the same bearings were compared and analyzed with different time distributions. A real-time prediction model of high-speed train bearing temperature anomalies based on Bi-directional Long Short-Term Memory (BILSTM) was proposed. Finally, the D-S evidence theory was used to combine the anomaly detection model based on the AHP entropy method and the anomaly detection model based on BILSTM real-time prediction. Through the comprehensive diagnosis and decision-making of high-speed train bearings from two dimensions of space and time, a more comprehensive and accurate anomaly detection model was realized. The experimental results showed that the spatiotemporal comparison fusion decision model successfully eliminated the misjudgment phenomenon of single-dimension model diagnosis and that it has good early warning ability.

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