Thursday, December 5, 2024
Rail operations are always in motion, with train systems and networks undergoing constant change and improvement. However, they must maintain peak health to ensure safe, efficient, and timely service. So, what happens when these systems experience everyday wear and tear or more serious issues?
Much like doctors, train maintenance specialists review operational data to diagnose problems and propose solutions. This process can take time, particularly when dealing with vast amounts of data.
Ossee, who earned advanced degrees in artificial intelligence, joined Alstom in 2023 with the mission of bringing cutting-edge AI solutions to the rail industry. As a leader in AI for rail, Alstom is transforming operations across the sector, optimizing train scheduling, managing speeds, forecasting passenger demand, and improving asset management, signaling, and object detection.
Speeding up a painstaking task with AI
Our data scientists make it their business to work alongside engineers to develop a series of AI “super analysers,” that can halve the time taken to make diagnoses, while making them more accurate. “We are advancing fast in several applications of our AI diagnostics.” says Ossee Yiboe, Data Scientist at Alstom. “As part of everyday maintenance, data from trains and infrastructure is recorded chronologically in operation logs. When trains run smoothly, these logs are deleted automatically, but if something goes wrong, they are communicated in real time to drivers and troubleshooting experts.”
To prevent service interruptions, swift solutions are essential. Frequently, multiple maintenance actions are required before the correct solution is identified.
“Our goal is to use AI to create tools that help experts quickly find and fix system issues by analysing logs”, adds Ossee.
To make the process more efficient, AI models based on existing data sets recognise patterns, identify causes and root causes and suggest solutions to technicians on the ground. “In a particular example, our solution analyses around one thousand system log variables to identify the most likely causes of a problem and potential remedies, effectively narrowing the failure environment down to just a dozen probable causes. By leveraging this AI technology, organisations can significantly speed up troubleshooting by 8 times, thereby enhancing overall maintenance productivity.”
Making maintenance more efficient
AI-driven solutions can provide results with 90% accuracy when identifying the reasons of failure, a great support for less experienced maintainers. “At the end of the day we’re looking for cause and effect,” Ossee says. “Using interesting new techniques, we can learn from hundreds of variables and narrow them down to the one that has caused the failure. Each use case we develop will help diagnose future issues and make maintenance more efficient.”
