Bradley Fighting Vehicle (Sgt. Brandon Banzhaf/US Army)

Machine learning to predict component failure

The Army will be using machine learning software to predict when components on the Bradley Fighting Vehicle need maintenance.

Through an award facilitated by Defense Innovation Unit Experimental, the Army will be working with Uptake, a company that provides artificial intelligence solutions for industrial sector clients, to predict component failures, decrease the frequency of unscheduled maintenance and improve the productivity of repair operations.  

“The Bradley already has sensors throughout it on major components of systems, so what we’ll be doing is capturing that data that is generated all throughout the Bradley and then combining it with our software to provide insights on potential failures before they happen,” said Matthew Lehner, a spokesperson for Uptake.

None of the company’s software is uploaded to the military vehicle. The data from the sensors is transmitted to the cloud, where Uptake's software analyzes normal operational patterns and learns to predict failures.

The company will also leverage the billions of hours of operating data it has collected from the other industries it serves, Lehner said. “We have 230 million hours of data on diesel combustion engines, which is something the Bradley also has,” he said.

The Uptake interface provides insight on individual vehicles. If a fault is found, then it is listed along with a fault code, a description, the severity (low, medium, critical), and the first and last occurrence of that particular fault.

The interface largely stays the same from industry to industry, but the machine learning models have to be changed and require a verification period before full deployment, Lehner said.

In 2016, the Army Materiel Command's Logistics Support Activity worked on a similar project with IBM that used the company's Watson artificial intelligence platform to help predict maintenance problems in Stryker combat vehicles.

About the Author

Matt Leonard is a reporter/producer at GCN.

Before joining GCN, Leonard worked as a local reporter for The Smithfield Times in southeastern Virginia. In his time there he wrote about town council meetings, local crime and what to do if a beaver dam floods your back yard. Over the last few years, he has spent time at The Commonwealth Times, The Denver Post and WTVR-CBS 6. He is a graduate of Virginia Commonwealth University, where he received the faculty award for print and online journalism.

Leonard can be contacted at mleonard@gcn.com or follow him on Twitter @Matt_Lnrd.

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