Fault Detection, Isolation, and Reconstruction for Satellite Attitude Sensors Using an Adaptive Hybrid Method

Abstract

As a typical safety-critical system, satellite has extremely stringent requirements for reliability. However, some satellite components, especially attitude sensors, are prone to suffer from performance degradation or even drastic failure in space environment, which leads to serious threats to satellite. A series of fault diagnosis methods have been investigated to diagnose these failures. However, due to the increasingly complex structure and various operating conditions of satellite, it is still a challenging task to diagnose attitude sensor failures robustly and reliably. In addition, sophisticated diagnosis methods result in heavy calculations, which may not satisfy satellite autonomy requirements. Given these issues, this article proposes an integrated fault detection, isolation, and reconstruction (FDIR) scheme for attitude sensors, such as fiber-optic gyroscopes (FOGs) and star sensor, using an adaptive hybrid method. The parity equation (PE) approach is applied to detect gyros fault. On the basis of attitude kinematics model and sensor measurement equation, a novel adaptive extended Kalman filter (EKF) is developed simultaneously. Then, the chi-square test is established using an innovation sequence for detecting sensor fault. According to fault detection results, different reconstruction strategies are presented by autonomously tuning noise covariance parameters, which will be used to reconstruct the faults of gyros and star sensor. Combined with the PE approach and chi-square test, faults in gyroscopes and star sensor can be detected reliably. Then, using this adaptive algorithm, these failures can be isolated and reconstructed accurately. Finally, the effectiveness of the presented method is verified by simulating typical faults of gyros and star sensor.