Amey is one of the UK’s leading engineering asset management companies. We manage the design, build and maintenance of large public infrastructure estates – our clients include rail operators, airports and public utilities. The assets are specified for long lifetimes – an escalator is designed to last 30 years; a bridge hundreds.
Many of these assets are instrumented via a variety of legacy systems. We have designed and deployed a system called Mercury, which builds models of asset performance from this instrumentation data, and combines it with work order and other maintenance data to allow operations and maintenance teams to understand the performance of their assets.
Machine learning is integral to Mercury, problems include free text matching, anomaly detection and fault prediction. I will talk about our experiences of applying ML techniques into legacy asset datasets, the issues we have faced, and how we have been able to provide actionable predictions of upcoming asset failures.
Dr Stephen Gooberman-Hill is a Principal Consultant in Amey’s Strategic Consulting and Technology Group. He is the solution originator of Amey’s Mercury data analytics system. He is currently managing a number of Mercury pilot deployments, and is also developing innovative data gathering and analytic solutions across a range of customers and partners.