By Major General Kurt Stein, U.S. Army retired, and Raytheon Technologies Senior Logistics Adviser

What if your car could tell you exactly when it will need repairs?

Using artificial intelligence and machine learning, your car’s computer could one day analyze your driving habits and predict when you'll have to bring it in for service. It would also factor in variables like engine age, weight, road conditions, oil viscosity, idle time and mileage, then use advanced algorithms to tell you when it will be time for a tuneup or to change the timing belt.

What works for your car could also work for a fighting vehicle. Various branches of the military, including the U.S. Army, are investigating predictive and prognostic maintenance for fleets of vehicles, using AI and machine learning to flag failing parts and systems before they break down.

Today, if something breaks, we fix it. That’s the way the services have done things for a long time – reactive maintenance. Now, they’re working to move beyond failure analysis to anticipate failures before they become problems.

At Raytheon Intelligence & Space, a Raytheon Technologies business, we have expertise in data analytics and maintaining military equipment, and have demonstrated that data-driven technology can keep fleets up and running with minimal downtime. The company is collaborating with several commercial companies to upload diagnostic data from sensors already aboard the Army's Bradley Fighting Vehicle and M88 Recovery Vehicle, and forecast equipment failures before they occur. We’re introducing these principles on other programs and platforms with other Service branches as well. The results show meaningful reductions in downtime and cost, and increases in technical efficiency of platforms.

If the data-driven system signals that a part may become faulty, commanders can repair or replace it before the entire vehicle is compromised. It also gives commanders a better sense of the operational readiness of their vehicles.

Currently, if a commander has a dozen Bradleys on hand, for instance, but needs three of them for a specific mission, choosing which ones are most likely to succeed is based on intuition…a guess. With real-time data, commanders could choose the vehicles that are the most operational and mission-ready.

A data-driven system could also help reduce the supply chain and logistical footprint at forward-deployed locations, or the “edge,” as it’s called.

Today, the Army is using historical data to decide what parts they should bring when deployed. With AI, machine learning and analytics, Army maintainers can predict what they’ll really need. Instead of dragging along 120 parts, they might need to take only 80 parts. And these will be parts they need, not just good to have.

The technology could also save the Army, Marines, and other branches time and money by avoiding unnecessary preventive maintenance. Every Bradley gets a weekly maintenance check plus quarterly, semi-annual and annual inspections.

We know that different parts fail at different rates depending on the environment and the type of operation it’s conducting,. If it’s the desert, we know sand takes its toll on components, and if it’s gunnery operations, fire control systems will fail faster. Now, we’ll have data to make those informed decisions on when and what needs to be fixed.

While cost savings, efficiency and a smaller logistical footprint are all benefits of the new technology, mission readiness and soldier safety are the top priorities.

If a component could potentially break based on history, analytics, data and fact, then we can avoid putting a solider or Marine in harm’s way by leveraging technology to choose a different vehicle or swap out a component. In some cases, troops have to go with what they’ve got. But this capability provides leaders with better information to make an informed decision on whether to send troops into combat.

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