# Real-Time Hybrid Deep Learning-Based Train Running Safety Prediction Framework of Railway Vehicle

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## Abstract

**:**

## 1. Introduction

## 2. Background and Literature Review

## 3. Results Train Running Safety Data and Measurement Framework

## 4. Real-Time Deep-Learning-Based Train Running Safety Prediction Framework

## 5. Verification and Analysis of Hybrid Deep-Learning Prediction Framework for Train Running Safety

## 6. Conclusions and Further Study

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 2.**Allowance of dynamic running safety: (

**a**) Allowance per derailment coefficient (DC); (

**b**) allowance of running safety per dynamic vertical power (DV).

**Figure 5.**The target railway and its rail model: (

**a**) The target real railway; (

**b**) track distance plot of the modelled railway.

**Figure 6.**Vibrations from the transient analysis using the modeled railway and HEMU-430X: (

**a**) Vibration plot; (

**b**) model data and output data using the transient analysis.

**Figure 7.**Statistical correlation test among 28 attributes. (

**a**) Correlation test between attributes. (

**b**) Correlation between “Railway point” and “Right wheel derail coefficient (DC)”.

**Figure 8.**Data plot of the “Wheel lateral pressure” from a transient analysis: (

**a**) Data plot of wheel lateral pressure; (

**b**) standard deviation plot of wheel lateral pressure.

**Figure 9.**A general deep neural network architecture and stationary characteristics of output variables. (

**a**) A general deep neural network architecture. (

**b**) Stationary characteristics of output variables in train running safety.

**Figure 11.**Comparisons between both frameworks: (

**a**) RMSE using the DNN model; (

**b**) loss using the DNN model; (

**c**) RMSE using the proposed hybrid deep learning framework; (

**d**) loss using the proposed hybrid deep learning framework.

**Figure 12.**Prediction results using the test set: (

**a**) Prediction results using the DNN model; (

**b**) prediction results using the DNN model.

Symbol | Terms | Unit |
---|---|---|

L | Lateral force | kN |

V | Vertical force | kN |

N | Normal force | kN |

$\mathsf{\alpha}$ | Contact angle (Flange contact angle) | $\xb0$ |

${\mathrm{T}}_{\mathrm{Y}}$ | Tangential force | kN |

Y | Lateral force per a wheel axis | kN |

P | Axle load | kN |

$\mathsf{\mu}$ | Friction coefficient | $\mathsf{\mu}\in \mathrm{R}$ (R is real number) |

$\mathsf{\Delta}\mathrm{V}$ | Gap between consecutive vertical forces | kN |

Classification | Measurements | Unit | Criteria |
---|---|---|---|

Train running safety | Rate of wheel load reduction (DV) | $\mathrm{R}\in \left[0,1\right]$, R is a real number | $\mathrm{DV}\le 0.13$ |

Derailment coefficient (DC) | R | $\mathrm{DC}\le 0.8$ | |

Lateral displacement of rail head (LD) | mm | $\mathrm{LD}\le 4$ |

Existing Research Studies | Characteristics | Used Methods | Issues |
---|---|---|---|

Arvidsson et al. [9] | - Running safety simulation under non-ballasted bridge environments - Simulation analysis of running safety and passenger comport | - Simulation using 2D train-track-bridge model | - Predefined model-based simulation studies |

Ding, et al. [10] | - Early warning framework with vibrations of an express train | - Nonlinear equation-based regress model | - Monitoring-based early warning framework |

Choi, et al. [11] | - Light rail (LRT)-based vibration measurement on real running environment | - Real measurement | - Limited in small distance-measurement |

Jang and Yang [12] | - Numerical simulation—Consideration on transition between floating slab track and concrete track | - DIASTARS-based CAE simulation | - Limited experimental condition |

Kim, et al. [13] | - CAE-based simulation studies | - Input of “real railway models and conditions” | - CAE-based analysis |

Oh and Kwon [14] | - Measure on real train - Exemplary proof of DV’s importance on running safety | - Vibration measurement on trains with different weights | - Single factor (weight)-based experiment |

Seo, et al. [15] | - Simulation study- Relationship between train wheels and floating railway bridges | - Modeling of floating railway bridges - Nonlinear equation-based wheel motion model | - Nonlinear equation-based simulation model |

Zhang, et al. [16] | - 3D simulation model of train-induced vibration of a floating slab | - Train/environment model-based simulation | - Model-based simulation study |

Classification | Attribute | Unit | Data Source | |
---|---|---|---|---|

Modeling Input Form Real Measurement | Generation Using Transient Analysis | |||

Railway model data | Railway point (distance) | mm | O | - |

Cross level irregularity (cant) | mm | O | - | |

Curvature irregularity | 1/km | O | - | |

Lateral irregularity | mm | O | - | |

Vertical irregularity | mm | O | - | |

Gauge variation | mm | O | - | |

Train structure/ simulation data | Bogie upper frame lateral vibration | $\mathrm{m}/{\mathrm{s}}^{2}$ | - | O |

Bogie upper frame vertical vibration | $\mathrm{m}/{\mathrm{s}}^{2}$ | - | O | |

Bogie upper body lateral vibration | $\mathrm{m}/{\mathrm{s}}^{2}$ | - | O | |

Bogie upper body vertical vibration | $\mathrm{m}/{\mathrm{s}}^{2}$ | - | O | |

Left wheel lateral weight | kg | - | O | |

Right wheel lateral weight | kg | - | O | |

Left wheel vertical weight | kg | - | O | |

Right wheel vertical weight | kg | - | O | |

Left wheel derail coefficient (DC) | Real number | - | O | |

Right wheel derail coefficient (DC) | Real number | - | O | |

Left wheel rate of load reduction (DV) | Real number | - | O | |

Right wheel rate of load reduction (DV) | Real number | - | O | |

Body frame lateral pressure (body frame lateral forces) | kN | - | O | |

Left axle box lateral vibration | $\mathrm{m}/{\mathrm{s}}^{2}$ | - | O | |

Right axle box lateral vibration | $\mathrm{m}/{\mathrm{s}}^{2}$ | - | O | |

Left axle box vertical vibration | $\mathrm{m}/{\mathrm{s}}^{2}$ | - | O | |

Right axle box vertical vibration | $\mathrm{m}/{\mathrm{s}}^{2}$ | - | O | |

Wheel lateral pressure wheel lateral forces) | kN | - | O |

Attribute (Railway Model Parameters) | Relationships with Train Structure and Mechanism |
---|---|

Railway point (distance) | - Little relationship (r* < 0.01) |

Cant | - Weak relationship: Left axle box vertical vibration - little relationship with the other factors |

Curvature irregularity | - Little relationship (r* < 0.01) |

Lateral irregularity | - Strong relationship: Bogie upper frame lateral vibration - Weak relationship: Right wheel lateral weight, Right wheel DV, wheel lateral pressure - little relationship with the other factors |

Vertical irregularity | - Strong relationship: Bogie upper frame vertical vibration - Weak relationship: Left/right wheel vertical weight, Left/right wheel DC, Left/right axle box vertical vibration - little relationship with the other factors |

Gauge variation | - Weak relationship: Wheel lateral pressure - little relationship with the other factors |

Classification | A DNN without Recurrent Data | The Proposed Hybrid Network |
---|---|---|

Input | ${\mathrm{X}}_{\mathrm{i},\mathrm{i}\in \mathrm{N}\left[1,6\right]}\left(t\right)$, ${\mathrm{X}}_{\mathrm{i},\mathrm{i}\in \mathrm{N}\left[7,18\right]}\left(t\right)$ | ${\mathrm{X}}_{\mathrm{i},\mathrm{i}\in \mathrm{N}\left[1,18\right]}\left(t\right),$ ${\mathrm{X}}_{\mathrm{i},\mathrm{i}\in \mathrm{N}\left[7,18\right]}\left(t\right)$ ${\widehat{Y}}_{j}{\left(t-k\cdot \mathsf{\Delta}t\right)}_{j\in N\left[1,5\right]},k\in N\left[1,5\right]$ |

Output | ${\widehat{Y}}_{j}{\left(t\right)}_{j\in N\left[1,5\right]}$ | |

Layer architecture | 4 hidden layers Number of hidden nodes in each hidden layer = {40,30,15,5} | 4 hidden layers Number of hidden nodes in each hidden layer = {50,30,15,5} |

Activation functions | Sigmoid/ReLU Sigmoid: $\frac{1}{1+{\mathrm{e}}^{-\mathrm{x}}}$ RelU: max(0,x) | |

Learning parameters | Epoch = 5000/optimization method = ADAM () $\mathrm{Learning}\mathrm{rate}(\mathsf{\eta})$= 0.001 Dropout rate = 0.2 |

Classification | LSTM | DNN Using | The Proposed Framework |
---|---|---|---|

Input | ${\widehat{Y}}_{j}{\left(t-k\cdot \mathsf{\Delta}t\right)}_{j\in N\left[1,5\right]}$,$k\in N\left[1,5\right]$ | Refer Table 5 | Refer Table 5 |

Output | ${\widehat{Y}}_{j}{\left(t\right)}_{j\in N\left[1,5\right]}$ | ||

Parameters | Number of hidden dimensions = 20 State activation function = sigmoid Gate activation function = tanh Learning rate ($\mathsf{\eta}$) = 0.001 |

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## Share and Cite

**MDPI and ACS Style**

Lee, H.; Han, S.-Y.; Park, K.; Lee, H.; Kwon, T. Real-Time Hybrid Deep Learning-Based Train Running Safety Prediction Framework of Railway Vehicle. *Machines* **2021**, *9*, 130.
https://doi.org/10.3390/machines9070130

**AMA Style**

Lee H, Han S-Y, Park K, Lee H, Kwon T. Real-Time Hybrid Deep Learning-Based Train Running Safety Prediction Framework of Railway Vehicle. *Machines*. 2021; 9(7):130.
https://doi.org/10.3390/machines9070130

**Chicago/Turabian Style**

Lee, Hyunsoo, Seok-Youn Han, Keejun Park, Hoyoung Lee, and Taesoo Kwon. 2021. "Real-Time Hybrid Deep Learning-Based Train Running Safety Prediction Framework of Railway Vehicle" *Machines* 9, no. 7: 130.
https://doi.org/10.3390/machines9070130