Hybrid robust fault detection and isolation of satellite reaction wheel actuators

In this paper, a combined robust fault detection and isolation scheme is studied for satellite system subject to actuator faults, external disturbances, and parametric uncertainties. The proposed methodology incorporates a residual generation module, including a bank of filters, into an intelligent residual evaluation module. First, residual filters are designed based on an improved nonlinear differential algebraic approach so that they are not affected by external disturbances. The residual evaluation module is developed based on the suggested series and parallel forms. Further, a new ensemble classification scheme defined as blended learning integrates heterogeneous classifiers to enhance the performance. A wide range of simulations is carried out in a high-fidelity satellite simulator subject to the constant and time-varying actuator faults in the presence of disturbances, manoeuvres, uncertainties, and noises. The obtained results demonstrate the effectiveness of the proposed robust fault detection and isolation method compared to the traditional nonlinear differential algebraic approach

Hybrid robust fault detection and isolation of satellite reaction wheel actuators

In this paper, a combined robust fault detection and isolation scheme is studied for satellite system subject to actuator faults, external disturbances, and parametric uncertainties. The proposed methodology incorporates a residual generation module, including a bank of filters, into an intelligent residual evaluation module. First, residual filters are designed based on an improved nonlinear differential algebraic approach so that they are not affected by external disturbances. The residual evaluation module is developed based on the suggested series and parallel forms. Further, a new ensemble classification scheme defined as blended learning integrates heterogeneous classifiers to enhance the performance. A wide range of simulations is carried out in a high-fidelity satellite simulator subject to the constant and time-varying actuator faults in the presence of disturbances, manoeuvres, uncertainties, and noises. The obtained results demonstrate the effectiveness of the proposed robust fault detection and isolation method compared to the traditional nonlinear differential algebraic approach

Intelligent hybrid robust fault detection and isolation of reaction wheels in satellite attitude control system

This paper presents a novel hybrid scheme for robust detection and isolation of faults affecting control torques of reaction wheel motors in the satellite control system. The proposed fault diagnosis scheme consists of a residual generation module relying on a bank of residual filters, followed by an intelligent residual evaluation module. The residuals are designed to be decoupled from aerodynamic disturbance and maneuvers by exploiting a nonlinear geometric approach. The residual evaluation module is then implemented via two separate schemes arranged in series and parallel forms. In particular, in the series form the detection module detects the occurrence of a fault, whilst the isolation module identifies the occurred fault in cascade. On the other hand, the parallel form exploits a single module carrying out these tasks simultaneously. Furthermore, an ensemble classification scheme, defined as blended learning, is exploited along with geometric approach for the first time in this work. This strategy blends heterogeneous classification schemes to improve the fault classification performances. Extensive assessments on the performances and robustness properties of the presented methods are performed by a high-fidelity satellite simulator with respect to parameter uncertainties, attitude maneuvers, disturbances, and measurements errors. The results document that the suggested hybrid fault detection and isolation outperforms the classic nonlinear geometric approach