Deploying Machine Learning Anomaly Detection Models to Flight Ready AI Boards

This study explores the development and implementation of machine learning (ML) models on Edge-AI boards, aiming to identify the most effective solution for anomaly detection systems on space missions. We investigate ML anomaly detection techniques including Autoencoders, Long Short-Term Memory (LSTM) cells, Isolation Forests, and Transformers. These models were trained on a univariate dataset derived from real space missions and deployed on diverse hardware platforms engineered for space environments to comprehensively assess performance. Specifically, we explore space flight ready boards (Ubotica CogniSAT-XE1 and XE2, which incorporate Intel’s Myriad 2 and X chips, respectively); commercial, non-space flight ready, edge-AI boards (NVIDIA’s Jetson Nano and Google Coral); and Field Programmable Gate Array (FPGA) implementations (from Microchip, AMD, and NanoXplore). We compare the performance of anomaly detection models run on space flight ready and commercial boards (using CPU performance as a benchmark) to provide a thorough comparison of available platforms for onboard anomaly detection. This paper provides a detailed examination of both the optimal ML models and hardware platforms for deploying univariate anomaly detection systems in space flight contexts and draws conclusions about which ones are most suitable.