Posted on November 10, 2026 by Anastasia Cisneros-Lunsford
An Office of Naval Research award supports research into aircraft reliability while providing training opportunities for doctoral students.
Naval aircraft maintenance has largely followed fixed schedules for decades, with inspections and repairs performed at predetermined intervals. But a new research project led by David Han, PhD, a professor in UT San Antonio’s College of AI, Cyber and Computing, plans to determine whether a more data-driven approach could keep aircraft in service longer.
Han, Romo Endowed Professor and Graduate Advisor of Record for Master of Science in Data Analytics, has received a $510,000 grant from the U.S. Department of Defense’s Office of Naval Research to study how preventive maintenance affects the reliability and availability of naval aircraft. Using decades of maintenance records, he and his team of doctoral students will develop predictive models to help the Navy identify the right time for maintenance, reduce unexpected breakdowns, improve aircraft reliability and make maintenance operations more efficient.
“At its heart, the project asks a straightforward question: How can we determine when aircraft maintenance provides the greatest benefit?” Han explained. “Aircraft become older, accumulate flight hours, and experience different operating conditions. Preventive maintenance is intended to keep them reliable, but we need a clearer understanding of what happens afterward.”
How the approach differs from today’s standards
Currently, preventive maintenance is performed at scheduled intervals regardless of aircraft usage, operating conditions or maintenance history. While that approach ensures regular inspections and repairs, it can sometimes result in unnecessary maintenance or fail to prevent unexpected equipment problems.
Researchers will conduct a three-year project analyzing roughly 30 years of maintenance records. The team will also study aircraft age and usage patterns and examine the reliability of whole aircraft and specific systems. Han said the research project will bring together statistical modeling, data science, analytics, machine learning, AI, and reliability engineering to provide a more complete picture of aircraft system health. Rather than focusing only on predicting when failures might occur, the research will also examine how maintenance decisions influence future aircraft performance.
“A central research theme is understanding how systems age, why they fail, and how we can make better decisions about inspection and maintenance,” Han said. “I have applied these ideas to a range of problems, including aircraft reliability, renewable energy, and electric vehicles.”
Transparent models for better understanding
A key goal of the project is to develop models that are transparent and easy for decision-makers to understand. While some predictive systems generate recommendations without clearly explaining how they reach their conclusions, Han’s team intends to create tools that provide greater insight into the factors that influence aircraft reliability.
“We want decision-makers to understand which factors are associated with degradation, how maintenance timing affects reliability and how much uncertainty remains in those conclusions,” he said.
If successful, the research could help the Navy improve aircraft availability, reduce unscheduled maintenance and lower maintenance costs. Improved predictions could also help maintenance teams identify potential problems before they affect operations.
“The goal is not simply to perform less maintenance,” Han said. “It is to perform the right maintenance at the right time. Our models are intended to support maintenance experts and improve existing decision processes, not replace their judgment.”
Student mentorship goals
Han said one of the most rewarding aspects of the research project is connecting with student mentorship. This project will provide doctoral students at UT San Antonio with hands-on research opportunities. Students will contribute to multiple phases of work, from organizing maintenance data to developing and testing predictive models.
“(Students) will have the opportunity to see how statistical concepts translate into decisions with real-world consequences,” Han said. “For me, this project is an opportunity to bring rigorous research, collaboration and student training together around an important national problem.”
Ultimately, Han hopes the research will lead to more reliable, efficient maintenance practices while keeping aircraft mission-ready.
“Our goal is to help the Navy get more dependable service from its aircraft and greater value from its maintenance resources, while keeping safety central to the decision-making process,” he said.
— Anastasia Cisneros-Lunsford
