Axle Counting Device Using Reinforcement Learning for Track Occupancy
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Solution Overview
Problem
Existing axle counting methods for determining track section occupancy are unreliable, especially in curved or switch areas, and fail to adapt to changing train usage patterns, leading to potential collisions and inefficiencies.
Innovation Solution
The implementation of a computer-aided axle counting system using reinforcing learning algorithms, specifically neuronal networks, to optimize parameter settings for detecting wheel influences, allowing for adaptive and reliable occupancy determination across various track sections without prior knowledge of individual application situations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional axle counting methods are used, then the system is simple and cost-effective, but the reliability is insufficient especially in curved or switch areas
Solution Approach 1:
The system changes parameters dynamically by using reinforcement learning to continuously optimize detection thresholds and parameters based on actual operating conditions. The algorithm adjusts parameters such as vibration thresholds, time windows, and pattern recognition criteria to adapt to different track sections, curves, and switch areas, thereby improving reliability without requiring complex hardware modifications
Solution Approach 2:
The axle counting system performs self-optimization through the reinforcement learning algorithm that automatically learns and adapts to specific track conditions without external intervention. The system uses historical data and continuous feedback to self-adjust detection parameters, eliminating the need for manual calibration and reducing dependency on expert knowledge for each track section
2Adaptability or versatility
If fixed parameter settings are used for axle counting, then the system is simple to operate, but it cannot adapt to changing train usage patterns and individual track section requirements
Solution Approach 1:
The system transitions from static fixed parameters to dynamic adaptive parameters through reinforcement learning. The detection parameters are continuously updated based on real-time performance feedback and changing operational conditions, allowing the system to adapt to different train types, speeds, and track conditions while maintaining a unified hardware platform
Solution Approach 2:
The reinforcement learning algorithm implements continuous feedback loops where occupancy determination results are fed back to optimize future detections. The system learns from correct and incorrect detections, adjusting parameters to improve accuracy over time for each specific track section, thereby achieving high adaptability without requiring complex manual configuration
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the reliability and adaptability of axle counting, reducing inaccuracies and costs by automatically optimizing the system during operation, ensuring consistent surveillance and adjusting to changing conditions, thereby improving the overall reliability of train operations.
Implementation Method 1
the physical wheel influence of trains traveling on the track section is converted into a reference voltage
Data Source
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AI summary
The invention relates to a method for computer-aided determination of the occupancy state of a track section (GA), wherein the physical wheel influence of the train (ZUG) travelling along the track section (GA) is converted into a rectified voltage, the rectified voltage is read in the form of raw data into a processor (PRC) via an analogue-to-digital converter (ADU), and an assessment takes place with the aid of at least one algorithm in order to examine whether or not a wheel influence is present, on which basis an influencing state is derived. The influencing state is output in order to determine the occupancy state. The algorithm uses consolidating learning to optimise the parameters taken into consideration for the identification to identify wheel influences within the scope of the assessment. The invention also relates to an axle counting device (AZE) for identifying wheel influences on a track section (GA), and to a computer program product.