Increasing Flight Time With Predictive Engine Maintenance
Emerging Technologies: Machine Learning
We used machine learning to show the Air Force how to boost the reliability of their engines, predict part failure and save money
The Challenge
Improve Aircraft Uptime and Planning Capabilities
Last year alone, 150 J85 engines were repaired, reinstalled on aircraft and within 2 flying hours, they had to be removed from the aircraft to be repaired again.
The inability to predict and prevent engine parts failure was becoming ever more costly for the Air Force. They needed a tool predicting likelihood of failure and optimizing cost; a tool that would enable mechanics to proactively replace vulnerable parts while the engine is already in the shop. The tool had to be accurate, actionable, extensible, and ultra-simple to use.
Extremely clear and actionable interface.
The Results
Maximizing the number of missions the aircraft can fly before the next repair.
We created a hands-on tool for the maintenance shop chiefs leveraging IBM’s Watson predictive machine learning capabilities. The tool predicts which parts will soon fail on an engine, and indicates when replacement is most cost-effective, maximizing the number of missions the aircraft can fly before the next repair. Detailed part replacement guidance displays in an extremely clear and actionable interface.
With the core functionality established, we will extend capabilities to support and optimize between multiple engines. The tool will support more engine types, optimize engines across bases, and incorporate complex data inputs such as weather, geography and pilot flying style into its recommendations.
Extremely clear and actionable interface.
Service Offerings
Services Used
Technology Used
- Atlassian (Jira, Confluence, Bitbucket)
- Azure Resource Manager
- Azure App Service
- Microsoft OMS
- BLOB Storage
