Neural Parameter Estimators for hybrid fault diagnosis and estimation in nonlinear systems

This paper presents a novel hybrid fault diagnosis approach to detect and estimate component faults in general nonlinear systems with full-state measurement. Unlike most existing fault diagnosis techniques, the proposed solution provides an integrated framework to simultaneously detect, isolate, and estimate the severity of faults in system components. The proposed solution consists of a bank of adaptive Neural Parameter Estimators (NPE) where each NPE in the bank is designed based on a separate parameterized fault model. Each NPE in the bank estimates its corresponding unknown Fault Parameter (FP) that is further used for fault detection and estimation purposes. Fast convergence and simple isolation policy are among the characteristic features of our proposed solution. Static neural network architecture and simple weight adaptation laws also make the proposed technique appropriate for real-time implementations. Simulation results reveal the effectiveness of the developed scheme in detecting, isolating and estimating faults in components of reaction wheel actuators of a 3-axis stabilized satellite even in presence of satellite disturbances.