Spacecraft operate in harsh environments with relevant computational constraints as well as limited communication to Earth. Traditional Fault Detection, Isolation, and Recovery (FDIR) systems rely on pre-programmed thresholds, which can miss unforeseen anomalies, thus requiring frequent human intervention from ground. This paper explores the potential of onboard Artificial Intelligence (AI) for enhanced FDIR. By continuously analyzing spacecraft telemetry data, onboard AI can detect anomalies in real-time, enabling faster and more precise responses. This approach promises to revolutionize spacecraft operations by improving autonomy, reducing reliance on ground intervention, and ensuring mission success. The paper discusses how Machine Learning based methods can enhance spacecraft Fault Detection capabilities. In particular, the performance of methods such as Principal Component Analysis, Autoregressive model, Autoencoder, and Long Short-Term Memory networks are explored. The considered use-case is based on telemetry data from Reaction Wheels, thus the AOC subsystem. This work also addresses challenges associated with implementing onboard AI, such as computational resource constraints and radiation hardening. Finally, a performance comparison between the selected methods in different conditions closes the discussion.