For the young participants of the IFAB 4 Next Generation Talents program, it’s time to tackle the second challenge of the training journey: this time, the challenge comes from Alstom, a member company of the Foundation.
The new challenge focuses on a current and concrete issue, familiar to anyone who regularly travels by train: Alstom is asking participants to analyze train failures, one of the main causes of delays across the country. Specifically, the challenge requires students to improve the failure analysis process by automating the data collection and evaluation phase using Artificial Intelligence.
To address this challenge, students will be provided with a historical dataset as the basis for developing their solution. Finally, they will need to present use cases to demonstrate the real effectiveness of their proposed model.
This challenge will demand an innovative, pragmatic, and hands-on approach from participants. Understanding and validating the real-world applicability of their model will be a critical test for students as they turn their ideas into reality.
Challenge Overview
To analyze train failures—often the cause of frequent delays—students can follow these steps:
- Study and analyze the historical data provided by Alstom
- Design an AI model to automate data collection
- Verify the real-world applicability of the model
- Provide use case examples
