Rail Braking Deceleration Prediction Under Adverse Conditions
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Solution Overview
Problem
Existing braking systems in rail vehicles are impaired by adverse environmental conditions, limiting their deceleration performance and capacity utilization on rail networks.
Innovation Solution
A computer-implemented method using a neural network to predict the expected deceleration of a vehicle by inputting braking, environmental, and vehicle data, allowing for improved braking system performance even under adverse conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing braking systems are used in rail vehicles, then the braking function is provided, but the deceleration performance is impaired under adverse environmental conditions
Solution Approach 1:
The system performs preliminary actions by predicting expected deceleration values before actual braking events using neural networks trained on historical braking data and environmental conditions. This advance prediction allows the braking system to be pre-configured or adjusted optimally before adverse environmental conditions fully impact performance, thereby maintaining reliability despite environmental challenges
Solution Approach 2:
The system implements feedback by continuously monitoring actual braking performance, comparing it with predicted deceleration values, and using this information to refine future predictions and adjust braking strategies. This closed-loop feedback mechanism enables the system to adapt to varying environmental conditions and maintain optimal braking performance over time
2Productivity
If braking distance is reduced to increase line capacity, then productivity increases, but measurement precision of deceleration performance becomes more critical
Solution Approach 1:
The system replaces traditional mechanical braking control with an intelligent neural network-based prediction system. This substitution enables more precise deceleration forecasting by analyzing complex patterns in braking data and environmental conditions, providing the measurement precision needed to safely reduce braking distances and increase line capacity
Solution Approach 2:
The system dynamically changes operational parameters by adjusting predicted deceleration values based on real-time environmental conditions, vehicle characteristics, and braking history. This parameter adaptation allows optimization of braking distances for increased productivity while maintaining safety through accurate, condition-specific predictions
Data Source
AI summary
A computer-implemented method for predicting an expected deceleration of at least one vehicle, particularly at least one rail vehicle, the method comprising the steps of providing as input to a neural network at least one braking datum related to the performance of a braking system of the at least one vehicle, at least one environmental datum related to environmental conditions of a route along which the vehicle moves, at least one vehicle datum related to the structure of the at least one vehicle and through the neural network, predicting an expected deceleration value of the at least one vehicle, based on the at least one braking datum, at least one environmental datum and at least one vehicle datum.