ADCS Fault Detection and Isolation with Ensemble Machine Learning Techniques
Updated: Oct 17, 2019
Abstract - The primary objective of this study is to explore novel applications of data-driven machine learning methods for isolation of nonlinear systems with a case-study for an in-orbit closed-loop controlled satellite with reaction wheels as actuators. High-fidelity models of the 3-axis controlled satellite are developed to provide an abundance of data for both healthy and various faulty conditions of the satellite. This data is then used as input for the proposed data-driven fault isolation method. Once a fault is detected, the fault isolation module is activated where it employs a machine learning technique which incorporates ensemble methods involving random forests, decision trees, and nearest neighbours. Results of the classified faulty condition are then cross-validated using k-fold and leave-one-out methods. A comprehensive comparison of the performance of different combinations for the ensemble architecture. Results show promising outcomes for fault isolation of the non-linear systems using ensemble methods.