Contributed Session 3: Statistical Validation of Fuel Savings from In-Flight Data Recordings

Keltin joined the Software Engineering Institute's AI Division in June of 2023 as an Assistant Machine Learning Research Scientist after graduating from Carnegie Mellon University with a B.S. in Statistics and Machine Learning and an additional major in Computer Science. His previous research projects have included work on Machine Unlearning, adversarial attacks on ML systems, and ML for materials discovery.

The efficient use of energy is a critical challenge for any organization, but especially in aviation, where entities such as the United States Air Force operate on a global scale, using many millions of gallons of fuel per year and requiring a massive logistical network to maintain operational readiness. Even very small modifications to aircraft, whether it be physical, digital, or operational, can accumulate substantial changes in a fleet’s fuel consumption. We have developed a prototype system to quantify changes in fuel use due to the application of an intervention, with the purpose of informing decision-makers and promoting fuel-efficient practices. Given a set of in-flight sensor data from a certain type of aircraft and a list of sorties for which an intervention is present, we use statistical models of fuel consumption to provide confidence intervals for the true fuel efficiency improvements of the intervention. Our analysis shows that, for some aircraft, we can reliably detect the presence of interventions with as little as a 1% fuel rate improvement and only a few hundred sorties, enabling rapid mitigation of even relatively minor issues.

Session Materials: https://dataworks.testscience.org/wp-...