GTRI is deploying Artificial Intelligence (AI) and Machine Learning (ML) via a model-based systems engineering (MBSE) approach to optimize aircraft maintenance scheduling and risk management in DoD and commercial applications. Leveraging big data analytics and multivariable optimization methods, GTRI is coupling industry best practices with AI/ML to maximize aircraft readiness and minimize costs at global enterprise scale.

Understanding relationships between aggregated aircraft maintenance and routing data is critical to inform and automate business decisions in aerospace operations. GTRI is using AI/ML methods, particularly random forest regression and classification, on curated DoD and commercial data sets to predict maintenance costs and operational risks of aircraft assignments on flown and unflown flight schedules. By correlating aircraft risk values, maintenance resource requirements, and fleet reliability metrics, GTRI is developing an MBSE logistics framework and model to optimize readiness costs and profits/losses in a near-real time, automated, and dynamic manner.   

Specific technical material will include data collection, exploration, and processing, AI/ML technique selection (e.g., deciding between elastic net regularized regression, gradient boosting, and random forest), and advanced scheduling theory algorithms such as P||Cmax to minimize operational loads on aircraft.

This model will provide operations managers/planners recommended schedules and quantitative data to globally optimize the tactical execution of operations. Additionally, the models wholistic insight into operations and logistics complexities enable innovation, experimentation, and simulation for Condition Based Maintenance, irregular operations, and integration of applied technologies and commercial best practices.  
    
GTRI’s efforts directly support the Joint AI Center's Joint Logistics National Mission Initiative and commercial partnerships, specifically with Delta Air Lines, on advancing industry standards on maintenance and reliability programs (e.g, Maintenance Steering Group-4). Specific opportunities to apply this research to Naval/Marine aviation fleets in collaboration with NAVAIR will be discussed. Other GTRI research referenced in support of this abstract will include AI/ML virtual reconstruction of H-60 IVHMS data and AI/ML characterization and modeling of rocket plume performance.


AI Driven Cost-Wise Readiness: Machine Learning Applications in Aircraft Maintenance Scheduling Optimization and Risk Management


**Title:** Platform Readiness

**Moderator:** Kirk Horton, Naval Safety Command

               
        


Fleet Readiness - Q&A

technical paper

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Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

To spread awareness of current machine learning research relevant to Naval applications

To connect machine learning researchers with experts in military requirements

To build and strengthen collaborations within the defense research community


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NAML 2022

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop. The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

To spread awareness of current machine learning research relevant to Naval applications

To connect machine learning researchers with experts in military requirements

To build and strengthen collaborations within the defense research community



Kyle Blond

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AI Driven Cost-Wise Readiness: Machine Learning Applications in Aircraft Maintenance Scheduling Optimization and Risk Management

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