Artificial Intelligence Techniques for Spacecraft Health Monitoring System - A Survey

Artificial intelligence, including Machine learning, Deep learning, and Reinforcement learning, has shown successful results in various applications in the fields of science and engineering, such as electrical engineering, computer engineering, bioengineering, financial engineering, medicine, aerospace engineering, and more. From this point of view, researchers have turned to AI techniques to solve various challenges in their respective fields and have designed successful applications to overcome various challenges in the aerospace industry. The main concern of any space mission operation is to ensure the health and safety of the spacecraft. The worst case in this circumstance is probably the loss of a mission but the more common interruption of spacecraft functionality can result in compromised mission objectives. As spacecraft complexity rises, many present methods of system health monitoring are challenging to employ. Also, the possibilities to observe and interact with any given spacecraft are naturally limited compared to ground-based systems due to several factors. These include but are not limited to the availability and bandwidth of their connection to ground, the availability of staff, communication latencies, and power budgets. That’s the reason why every space-crafts need a minimum level of autonomy during their missions. The goal of this survey is to provide an overview of the world of artificial intelligence and its different methods, then talk about anomaly detection, Fault Detection Isolation and Recovery (FDIR), and a verity of methods of health monitoring systems and explain why it’s essential for every space missions.