Optimizing Predictive Maintenance: Enhanced AI and Backend Integration

TL;DR

This paper presents a cost-effective wireless sensor and machine learning-based system for predictive maintenance in rail transportation, improving reliability and efficiency especially in resource-limited rural areas.

Contribution

It introduces an integrated system combining sensors, secure data management, and machine learning tailored for rail maintenance, with a focus on stakeholder collaboration and infrastructure design.

Findings

Successful development of a sensor-based predictive maintenance system

Enhanced data security and integrity in rail monitoring

Potential reduction in maintenance costs and delays

Abstract

Rail transportation success depends on efficient maintenance to avoid delays and malfunctions, particularly in rural areas with limited resources. We propose a cost-effective wireless monitoring system that integrates sensors and machine learning to address these challenges. We developed a secure data management system, equipping train cars and rail sections with sensors to collect structural and environmental data. This data supports Predictive Maintenance by identifying potential issues before they lead to failures. Implementing this system requires a robust backend infrastructure for secure data transfer, storage, and analysis. Designed collaboratively with stakeholders, including the railroad company and project partners, our system is tailored to meet specific requirements while ensuring data integrity and security. This article discusses the reasoning behind our design choices, including the selection of sensors, data handling protocols, and Machine Learning models. We propose a system architecture for implementing the solution, covering aspects such as network topology and data processing workflows. Our approach aims to enhance the reliability and efficiency of rail transportation through advanced technological integration.