FlightEdge: Real-time, Distributed AI/ML Model Training for Predictive Maintenance

Our solution provides a real-time, distributed AI/ML framework for predictive maintenance (PM) of mission-critical assets, targeting increased operational readiness and reduced maintenance costs. By leveraging edge computing and federated learning, this approach allows predictive models to be trained across a network of assets without centralizing sensitive data, preserving both data integrity and security. Each asset in the system locally processes data, learns from real-time conditions, and contributes to an aggregated model, thereby improving PM insights across the entire fleet. This solution offers a scalable and secure architecture to continuously adapt and enhance predictive accuracy as assets operate in dynamic environments.áOur approach integrates Digital Materiel Management (DMM) principles, including Model-Based Systems Engineering (MBSE) and digital twins, to provide a secure, synchronized framework for distributed AI models. Digital twins of each asset enable real-time simulation and validation of model updates, ensuring robust performance and compliance before deployment. By simulating predictive model behaviors under operational scenarios, digital twins also facilitate rapid adjustment to changing mission profiles, environmental conditions, and asset wear-and-tear patterns, further enhancing maintenance accuracy and asset lifecycle management.The system is developed to support the DoDÆs Authority to Operate (ATO) requirements with the Risk Management Framework (RMF) in mind, embedding NIST 800-53 security controls into every phase. Secure data pipelines with end-to-end encryption, role-based access controls, and comprehensive logging provide stringent safeguards across all distributed nodes. The federated learning architecture reduces data exposure risk by keeping raw data local to each asset while only sharing secure model parameters. Continuous monitoring, automated vulnerability scanning, and anomaly detection support cybersecurity compliance, enabling a secure, adaptive learning system for real-time predictive maintenance.Using this distributed, secure AI/ML framework, the proposed solution advances CDAO mission requirements to enhance maintenance predictability and asset readiness across operational theaters. This capability will ultimately deliver an ATO-ready prototype capable of deploying within 18 months, designed to seamlessly integrate into existing DoD infrastructure while providing a resilient, scalable foundation for ongoing predictive maintenance improvements.