Design Interactive, Inc. — Department of Defense SBIR Phase I: N221-029

Description

Predictive maintenance (PdM) through artificial intelligence (AI) and machine learning (ML) is being widely evaluated and used across industries to improve standard maintenance scheduling and reduce time, expense, and downtime due to component failure. One area primed to benefit from an AI/ML PdM solution is Standard Missile (SM) maintenance. The SM maintenance process produces a fountain of data, but current utilization requires extensive man hours to analyze and does not provide aggregated insights. An opportunity exists to apply AI/ML advancements to the SM dataset, thereby optimizing SM maintenance strategies. Therefore, Design Interactive Inc. (DI) proposes the development of Aide for Missile Maintenance Operations (AMMO), a decision support tool that holistically optimizes SM maintenance strategies. AMMO will identify data for use in PdM modeling and prepare it for analysis. Then, AMMO will apply AI/ML concepts such as anomaly detection, classification, and survival regression to provide actionable insights on SM maintenance and reliability to the SM Program Office. These insights will be displayed within an intuitive decision support dashboard, allowing an at-a-glance view of sustainment activities and fleet readiness to support SM decision making. Implementation of AMMO is expected to increase automation of maintenance planning, reduce life cycle costs, and increase fleet readiness.