Work

A graph convolutional network for optimal intelligent predictive maintenance of railway tracks

Year: 2024

Type: article

Abstract: This study presents a prescriptive analytics framework for optimal intelligent predictive maintenance of railway tracks. We use machine learning and Graph Convolutional Networks (GCNs) to optimize the maintenance schedules for railway infrastructure and enhance operational efficiency and safety. The model leverages vast data, including geometric measurements and historical maintenance records, to predict potential track failures before occurrence. This proactive maintenance strategy promises to reduce downtime and extend the lifespan of railway assets. Through detailed computational experiments, the effectiveness of the proposed model is demonstrated, providing a significant step forward in applying advanced machine learning techniques to the maintenance of critical transportation infrastructures. • Propose a predictive maintenance framework for railway tracks leveraging AI innovation. • Harness graph convolutional networks and geometric insights to foresee track failures. • Develop proactive methodologies to minimize downtime and prolong track infrastructure. • Integrate cutting-edge machine learning to optimize safety and efficiency in maintenance.

Source: Decision Analytics Journal

DOI: 10.1016/j.dajour.2024.100542

Authors: Saeed MajidiParast, Rahimeh Neamatian Monemi, Shahin Gelareh

Institution: Université d'Artois

Language: English

Topic: Infrastructure Maintenance and Monitoring

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,500