A Hybrid Data-Driven Approach for Autonomous Fault Detection and Prognosis of a Spacecraft Reaction Wheel

This paper presents a hybrid data-driven approach for predicting the remaining useful life (RUL) of a spacecraft reaction wheel (RW). Our method combines a physics-informed model with a data-driven regression and machine learning technique known as the sparse identification of nonlinear dynamics (SINDy). This approach is used for fault detection and RUL prediction of the RW. For fault detection, we predict the states and health index (HI) parameters of the RW, with the coefficients of output torque and viscous friction selected as the HI parameters. To estimate the RUL, we analyze the trends of these HI parameters over time, predicting when the failure threshold will be crossed. We demonstrate that the proposed method is more effective and suitable for autonomous onboard applications compared to existing methods, such as Long Short-Term Memory (LSTM) recurrent neural networks.