We are delighted to announce that we are now working with Porterbrook, a British rolling stock company that provides high-quality, digitally-enabled rolling stock solutions to the rail industry. We have been selected to support the next phase of the Data to Action Reliability Taskforce (DART) engine project, which aims to improve engine reliability and performance by taking advantage of available sensor data. We will be combining multiple machine learning models to forecast engine component failure on the Porterbrook platforms in an operationally timely manner. This means that pre-emptive interventions can be taken at the optimal time to avoid unplanned failures whilst avoiding unnecessary engine downtime, what is called predictive maintenance.
We are starting from a solid base, adapting a Decision Lab UK’s owned AI capability Foresense, that we developed to predict failures of key systems on the Royal Navy’s type 45 destroyers. It is designed to handle the rarer failure events – the real challenge for predictive maintenance – as well as equipment components that are prone to failures. This project will therefore demonstrate how our technology adapts to different sectors and opens up predictive maintenance to more applications.
If you’re interested in seeing whether we can apply Foresense and advance ML techniques to bring predictive analytics to your problem, contact us!
