Fault Prediction in Planetary Drilling Using Subspace Analysis Techniques

In remote planetary environments, robotic agents must respond to or reason through faults before they escalate to mission-critical failures. No broadly applicable solution exists to give a specialized agent like The Regolith and Ice Drill for Exploring New Terrain (TRIDENT) situational awareness for when a situation may escalate to a drilling fault. We propose a new online time-series subspace analysis method, Entangled Singular Spectrum Transformation (ESST), to better predict and analyze faults using online data produced by the TRIDENT drill. We evaluate performance against other online subspace analysis techniques to determine the optimal detection method for sudden drilling faults.