Novel Non-Model-Based Fault Detection and Isolation of Satellite Reaction Wheels Based on a Mixed-Learning Fusion Framework

This paper suggests a model-free framework for Fault Detection and Isolation (FDI) of satellite reaction wheels for the first time. The proposed FDI method is based on multi-classifier fusion with diverse learning algorithms and configured in a parallel form where a unique module simultaneously performs both detection and isolation tasks. In other words, a multi-classifier-based arrangement is presented on the basis of Mixed Learning strategy where four classic and well-practised classification schemes including Random Forest, Support Vector Machine, Partial Least Square, and Naïve Bayes are incorporated into FDI module in order to make a decision on the occurrence of a fault and its location. Extensive simulation results with a high-fidelity nonlinear spacecraft simulator considering gyroscopic effects, measurement noise, and exogenous aerodynamic disturbance signals show that the proposed FDI scheme can cope with faults affecting reaction wheel torques and obtain promising FDI performances in most of the designed scenarios.