eMaintenance solutions for enhanced smart asset management in railway using Industrial AI

(AI Factory /RAILWAY)

Goal

The project aims to streamline and optimise asset management and rail maintenance, with a focus on railway infrastructure. From an academic perspective, the project will explore and investigate how technologies and methodologies in the so-called. 'Industrial Artificial Intelligence (IAI)', incl. deep learning, can be used to develop eMaintenance solutions that help to improve TAK, OEE and streamline the infrastructure management of railway infrastructure.

Projects status and results

The main objective of this thesis is to develop framework, approaches, technologies, and tools aimed to establish a platform that facilitates the asset management of railway system, through the utilization of Industrial AI and digitalisation.

The main results of this research are:

1. a taxonomy of issues and challenges related to asset management in railway system
2. an augmented asset management concept for railway system,
3. a MetaAnalyser platform for preliminary data analysis,
4. a fleet management approach to system-of-systems, and
5. a framework for context-aware now-casting and forecasting analytics for augmented asset management of railway system.

The work resulted in a licentiate thesis:

• Kumari, J. (2022). Augmented Asset Management of Railway System Empowered by Industrial AI.

The work also resulted in a doctoral thesis:

• Kumari, J. (2024). System-of-Systems Approach for Enhancing Asset Management of Railway System.