Improving U.S. Air Force Mission Capability with AI

The U.S. Air Force (USAF) operates the world’s largest, most technologically advanced, and powerful air fleet in the world. Maintaining and operating such a diverse fleet is mission-critical, complex, and costs tens of billions of dollars annually.

Yet, at any given time, many of these aircraft are not ready to fly.

A U.S. Government Accountability Office (GAO) report from 2022 found that aircraft mission-capable goals are usually not met. Of the 49 types of military aircraft the GAO studied, only four met their annual mission readiness goals for most of the years from FY 2011 through FY 2021. More than half of the aircraft types studied, 26 in all, did not meet their annual mission-capable goals in any fiscal year.

This is where AI can help. The mission of the Rapid Sustainment Office (RSO) is to optimize warfighter readiness by exploiting technologies to revolutionize sustainment operations. One of the key technology focus areas for the RSO is using artificial intelligence and machine learning to identify issues before they occur – such as identifying aircraft systems and components at risk of failure.

The RSO has set up the Condition-Based Maintenance Plus (CBM+) program office for applying artificial intelligence and machine learning to optimize fleet maintenance, increase aircraft availability, and minimize downtime. C3 AI has been an integral part of CBM+ since 2020 to:

• Develop the Predictive Analytics and Decision Assistant (PANDA) toolkit, powered by the C3 AI Platform and C3 AI Readiness application and hosted on Air Force Cloud One powered by AWS GovCloud, to identify aircraft systems and components at risk of failure, provide evidence for the need for proactive maintenance, and ensure availability of the right parts at the right place at the right time for maintenance.
• Meet all security requirements for the PANDA application to achieve Authorization to Operate enabling the application to be accessible to any user with a verified “need-to-know” and a DoD-issued Common Access Card (CAC).
• Scale the PANDA toolkit to monitor 3,110 aircraft across 16 aircraft platforms. The PANDA toolkit currently has 8 applications for 800+ active users.
• Highlight and scale the power of using sensor telemetry data to actively detect signs of degradation within an aircraft system or component.

C3 AI Readiness

Learn More

With PANDA and the CBM+ program office, the RSO and C3 AI have created a platform that helps:

Setting Component Useful Life Thresholds

Much of aircraft downtime is due to unexpected breakdowns of a system, subsystem, or component. If a replacement part is not immediately available, the aircraft could end up temporarily grounded. With the PANDA toolkit, part failures can be anticipated, so repairs are done proactively.

PANDA unifies data from multiple maintenance information systems to access historical data on maintenance actions and equipment status reports at component-level granularity. Much of this data is unstructured, and PANDA uses natural language processing AI/ML models to interpret the freeform maintenance text narratives to determine what maintenance actions were performed and determine time-to-failure for those components. This is used in the eRCM (enhanced Reliability Centered Maintenance) application to define the useful life threshold for each aircraft part i.e., the number of aircraft flying hours after which a component in service should be replaced. Armed with this intelligence, maintenance crews can maximize the use of a component over its useful life yet replace it before it fails