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Ziyi Wu¹, Wanying He², Ziyue Wu³

¹School of Economics and Management, Beijing Jiaotong University, Beijing, China. 524717937@qq.com.

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|November 17, 2025

Summary

This study introduces advanced image processing and AI for railway point machine condition assessment, enabling predictive maintenance. Optimized strategies significantly extend equipment lifespan and improve railway safety.

Keywords:

Aging model Health assessment Lifecycle cost analysis Maintenance strategy Markov chain Monte carlo simulation

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Area of Science:

Railway Engineering
Signal Systems
Predictive Maintenance

Background:

Current railway point machine maintenance relies on vague standards and rigid strategies.
Optimizing condition assessment and maintenance is crucial for railway safety.

Purpose of the Study:

To develop an intelligent system for point machine condition assessment and maintenance strategy optimization.
To enhance railway safety through predictive maintenance of critical signal equipment.

Main Methods:

Utilized image processing to extract current curves for condition assessment.
Developed a curve difference assessment model using PCHIP interpolation and RMSE.
Constructed an ageing model with maximum likelihood estimation and Markov chains for state prediction.

Main Results:

Classified switch machine states into five levels using the developed assessment model.
The fault repair strategy extended physical lifespan to 14.73 years, compared to 7.29 years for the maintenance strategy.
Economic lifespan differences between strategies were less than 4 years.

Conclusions:

The study provides a pathway for intelligent maintenance of railway signal equipment.
This approach facilitates a shift towards state prediction and precision repair for critical infrastructure.
Enhanced condition assessment and predictive maintenance improve overall railway operational efficiency and safety.