Framework for Data‒Driven Fault Diagnosis of Numerical Spacecraft Propulsion Systems

The increasing complexity of deep space missions introduces significant challenges in maintaining spacecraft health, particularly in the propulsion systems, due to the inherent communication delays with Earth. This research proposes a novel framework for the autonomous, data-driven fault diagnosis of spacecraft propulsion systems. Leveraging data generated from a spacecraft propulsion system simulation model. The study addresses the limitations imposed by computational resources and sensor installation constraints through Sequential Forward Selection (SFS) for optimized sensor placement and feature selection. The framework's effectiveness is demonstrated through implementation on a microcomputer, showing promising results in terms of diagnostic accuracy and processing speed, thus highlighting its potential for onboard spacecraft application. This study not only advances the autonomous capabilities of spacecraft in deep space but also contributes to the broader field of Prognostics and Health Management (PHM) by providing a scalable, efficient approach to fault diagnosis in critical spacecraft systems. The proposed framework demonstrates a promising approach to optimizing diagnostic tasks for spacecraft systems. However, the trade-offs observed necessitate a careful consideration of task-specific requirements and the potential need for adjustments to maintain a high level of accuracy alongside computational efficiency.