Naval aviation continues to experience issues with Cartridge Activated Device/Propellant Activated Device (CAD/PAD) shortages, obsolescence, lot failures, and delays in production and shipping. Reliability Centered Maintenance (RCM) has proven inadequate for effectively managing the service life of the estimated 2M+ CAD/PAD assets in the existing inventory.

In this paper, we will demonstrate a Sensor-less Digital Twin of two specific CADs and PADs associated with the Navy Aircrew Common Ejection Seat (NACES) that forecasts the remaining useful life of specified devices. This predictive analytics toolset could then be utilized to facilitate a seamless transition to CAD/PAD Condition Based Maintenance (CBM) Service Life management. The specific scope included the MT29 (Parachute Deployment Rocket Motor) and WB15 (Cartridge Actuated Initiator).

To deliver meaningful projections on CAD/PAD system health, we use a physics-based, stochastic modeling method and following a proven approach to data collection, validation, creation, and processing. The digital twin solution generates service life predictions by estimating probability distributions of potential outcomes by accounting for random environment and operational variation over time.

This paper will also demonstrate how this approach resulted in successful development of a Digital Twin that parallels reality by specific fielded device and generates results consistent with expectations. Additionally, we demonstrate how this approach to modeling Digital Twins does not require additional aircraft sensor installations. Finally, this paper will show how the current Calendar Based Service Life could be transitioned to a Condition Based Service life to reduce safety risks and improve the acquisition process.

Keywords
ARCHITECTURE;CAD MODELS;CHARACTERIZING SYSTEM PERFORMANCE ;MODELING

Additional Keywords