Advancing rail safety: An onboard measurement system of rolling stock wheel flange wear based on dynamic machine learning algorithms

2025-08-21

AI Summary

To address low accuracy and susceptibility to thermal drift and dynamic noise in online monitoring of wheel flange wear depth, this paper proposes an onboard dynamic machine learning monitoring system. Methodologically, it fuses displacement and temperature sensor data, employs a real-time IIR filter to suppress nonlinear thermal drift and dynamic noise, and develops a dynamically auto-trained regression model; filter parameters are optimized via FFT, while edge acquisition, real-time transmission, and processing are implemented on an embedded IoT platform. Key contributions include a dynamic model update mechanism and an IIR-ML collaborative filtering architecture. Experimental results demonstrate a monitoring accuracy of 98.2% (post-filtering), low end-to-end latency, and effective identification of abnormal wear induced by track irregularities—significantly enhancing wheel-rail condition awareness and railway operational safety.

Technology Category

• Machine Learning: Hardware-aware ML
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Rail and wheel interaction functionality is pivotal to the railway system safety, requiring accurate measurement systems for optimal safety monitoring operation. This paper introduces an innovative onboard measurement system for monitoring wheel flange wear depth, utilizing displacement and temperature sensors. Laboratory experiments are conducted to emulate wheel flange wear depth and surrounding temperature fluctuations in different periods of time. Employing collected data, the training of machine learning algorithms that are based on regression models, is dynamically automated. Further experimentation results, using standards procedures, validate the system's efficacy. To enhance accuracy, an infinite impulse response filter (IIR) that mitigates vehicle dynamics and sensor noise is designed. Filter parameters were computed based on specifications derived from a Fast Fourier Transform analysis of locomotive simulations and emulation experiments data. The results show that the dynamic machine learning algorithm effectively counter sensor nonlinear response to temperature effects, achieving an accuracy of 96.5 %, with a minimal runtime. The real-time noise reduction via IIR filter enhances the accuracy up to 98.2 %. Integrated with railway communication embedded systems such as Internet of Things devices, this advanced monitoring system offers unparalleled real-time insights into wheel flange wear and track irregular conditions that cause it, ensuring heightened safety and efficiency in railway systems operations.

Problem

• Develops onboard system to monitor wheel flange wear depth
• Uses machine learning to counter sensor nonlinear temperature effects
• Implements IIR filter for real-time noise reduction enhancing accuracy

Innovation

• Onboard system using dynamic machine learning algorithms
• IIR filter for real-time noise reduction enhancement
• Integration with IoT for real-time wear monitoring