Demonstrating the feasibility of applying machine learning models to railway condition data: Engine condition monitoring and failure prediction

The rail industry is undergoing a digital revolution, which has created opportunities to transform the way we are able to interact with older "mid-life" trains. Historically it has been technically and economically challenging to harness data from older assets - which is invaluable for understanding asset condition, optimising maintenance and predicting emerging failures.

Whilst digital enhancements in recent years have rapidly accelerated the ability to extract valuable data from these trains, this presents a new challenge to make sense of the mass of data produced from many disparate systems.

This project will combine asset knowledge, operational expertise, Data Science capability and the application of Machine Learning tools borne from the Aerospace sector to test the feasibility of using AI and Machine learning tools to extract insights from large amounts of near real-time data from train engines.

Chrome Angel Solutions and Amygda Labs are working in collaboration with Angel Trains and Grand Central Trains to explore the feasibility of applying Amygda's innovative machine learning tools to derive insights from engine data. Amygda Labs' unique approach to building ML models using unsupervised learning techniques enables faster of delivery of insights when compared to established approaches, typically reducing the model build time from months to days without relying on domain knowledge, which can be costly and time-consuming.

This feasibility study will test whether AI and Machine Learning tools can derive faster and deeper insights compared to current Data Science methods, by detecting relationships in the data and enabling faster and more proactive decision making, leading to better planning and improved asset availability.