High-Speed Train Emergency Brake Modeling and Online Identification of Time-Varying Parameters

By analyzing the mechanism of pure air emergency brake for high-speed train, the discrete emergency brake model is established. Aiming at the problem that time-varying hidden parameters cannot be observed directly, the sliding window-based expectation maximization is proposed, and the unobserved time-varying brake parameters are identified. Firstly, the position and size of the sliding window are selected; then, the sliding window-based expectation maximization is used for brake parameter identification; finally, combined with the gradient optimization, the optimal identifications of emergency brake parameters are obtained. The simulation results show that the brake parameters can be identified quickly and accurately by the proposed method. Under uniform noise, the identification errors of friction coefficient and braking ratio are ±0.0068 and ±0.0349, respectively, and the maximum relative errors between the identifications and true values are 2.4807% and 1.3154%, respectively, which can meet the actual requirements of the brake system. The effectiveness and practicability of the proposed model and method are verified.

[1]  Moongu Jeon,et al.  Adaptive Sliding-Window Strategy for Vehicle Detection in Highway Environments , 2016, IEEE Transactions on Intelligent Transportation Systems.

[2]  Guo Hong-g.  Hammerstein Model and Parameters Identification of EMU Braking System , 2014 .

[3]  Tao Tang,et al.  A Cooperative Train Control Model for Energy Saving , 2015, IEEE Transactions on Intelligent Transportation Systems.

[4]  Frank L. Lewis,et al.  Distributed Fault-Tolerant Control of Virtually and Physically Interconnected Systems With Application to High-Speed Trains Under Traction/Braking Failures , 2016, IEEE Transactions on Intelligent Transportation Systems.

[5]  Peng Li,et al.  Parallel processing algorithm for railway signal fault diagnosis data based on cloud computing , 2018, Future Gener. Comput. Syst..

[6]  Bo Tang,et al.  Intelligent Fault Diagnosis of the High-Speed Train With Big Data Based on Deep Neural Networks , 2017, IEEE Transactions on Industrial Informatics.

[7]  Xingxing Jiang,et al.  A Fast Signal Estimation Method Based on Probability Density Functions for Fault Feature Extraction of Rolling Bearings , 2019, Applied Sciences.

[8]  Xin Li,et al.  Estimating the Probability Density Function of Remaining Useful Life for Wiener Degradation Process with Uncertain Parameters , 2019, International Journal of Control, Automation and Systems.

[9]  Guo Xie,et al.  Adaptive Transition Probability Matrix-Based Parallel IMM Algorithm , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[10]  Yongduan Song,et al.  Fault-Tolerant Adaptive Control of High-Speed Trains Under Traction/Braking Failures: A Virtual Parameter-Based Approach , 2014, IEEE Transactions on Intelligent Transportation Systems.

[11]  Chin-Hui Lee,et al.  Parametric Dependencies of Sliding Window Correlation , 2018, IEEE Transactions on Biomedical Engineering.

[12]  Jian Zhang,et al.  Identification of key design parameters of high-speed train for optimal design , 2014 .

[13]  Xia Zhang,et al.  Standard Analysis for Transfer Delay in CTCS-3 , 2017 .

[14]  Chen De-wang,et al.  Modeling and System Identification of the Braking System of Urban Rail Vehicles , 2011 .

[15]  Yuan Cao,et al.  Research on dynamic nonlinear input prediction of fault diagnosis based on fractional differential operator equation in high-speed train control system. , 2019, Chaos.

[16]  Qi Song,et al.  Data-Based Fault-Tolerant Control of High-Speed Trains With Traction/Braking Notch Nonlinearities and Actuator Failures , 2011, IEEE Transactions on Neural Networks.

[17]  Yuan Cao,et al.  Application of fuzzy predictive control technology in automatic train operation , 2018, Cluster Computing.

[18]  Thomas B. Schön,et al.  System identification of nonlinear state-space models , 2011, Autom..

[19]  Dapeng Zhang,et al.  Optimal Design of the Absolute Positioning Sensor for a High-Speed Maglev Train and Research on Its Fault Diagnosis , 2012, Sensors.

[20]  Guo Xie,et al.  Modeling of Train Braking Based on Environment and Online Identification of Time Varying Parameters , 2018, 2018 International Conference on Control, Automation and Information Sciences (ICCAIS).

[21]  Narayan C. Kar,et al.  Expectation-Maximization Particle-Filter- and Kalman-Filter-Based Permanent Magnet Temperature Estimation for PMSM Condition Monitoring Using High-Frequency Signal Injection , 2017, IEEE Transactions on Industrial Informatics.

[22]  Youmin Zhang,et al.  Deep model integrated with data correlation analysis for multiple intermittent faults diagnosis. , 2019, ISA transactions.

[23]  Cristina Castejón,et al.  EMD-Based Methodology for the Identification of a High-Speed Train Running in a Gear Operating State , 2018, Sensors.

[24]  Ketan Rajawat,et al.  Tracking Moving Agents via Inexact Online Gradient Descent Algorithm , 2017, IEEE Journal of Selected Topics in Signal Processing.

[25]  Yongduan Song,et al.  Computationally Inexpensive Tracking Control of High-Speed Trains With Traction/Braking Saturation , 2011, IEEE Transactions on Intelligent Transportation Systems.