StandardAero has secured an award from the US Air Force (USAF) to deploy its Maintenance Insight predictive maintenance and reliability tools on the F100-220 engine programme, which powers more than 175 F-15 aircraft.
The award expands StandardAero’s Maintenance Insight support to the fifth engine type in the USAF fleet, strengthening the company’s role in applying data-driven maintenance technologies to military propulsion systems.
Maintenance Insight is a suite of predictive maintenance and diagnostic tools developed over two decades to support major defence platforms. Hosted within the USAF’s cloud computing environment, the system combines artificial intelligence, machine learning and digital twin technologies to analyse engine failure modes and generate predictive insights.
The technology provides propulsion teams with real-time information to support maintenance work scoping, deployment planning and preventative maintenance decisions. The objective is to improve aircraft readiness and engine reliability while helping reduce maintenance costs and increase time on wing.
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Marc Drobny, President of StandardAero’s Military, Helicopters and Energy Division, said the company’s work with the USAF has supported the development of predictive maintenance capabilities across multiple engine platforms.
StandardAero has previously worked with the USAF Propulsion team to develop diagnostic and predictive maintenance applications for the T56, which powers the C-130 fleet; the J85, used on the T-38; the TF33, which powers the B-52; and the TF39 engine.
The addition of the F100-220 programme broadens the application of Maintenance Insight across the USAF fleet. StandardAero said it intends to explore opportunities to extend the predictive maintenance solution to additional military engine platforms as well as commercial aerospace fleets.
The F100-220 powers more than 175 F-15 aircraft, making the programme a significant application for predictive maintenance technology aimed at improving fleet availability, reliability and maintenance planning.
