Non-linear Model-based Stochastic Fault Diagnosis of 2 DoF Helicopter

Abstract

Fault diagnosis of non-linear helicopter systems are affected by inherent characteristics such as nonlinear behavior and high cross coupling effects and external disturbances such as atmospheric turbulence and wind effects. This paper deals with the detection, identification and classicfiation of sensor, actuator and component faults in helicopter systems using model-based state estimation approaches. Approaches include Interacting Multiple Model based Extended Kalman Filter and Interacting Multiple Model based Unscented Kalman Filter. To address problem of fault detection, residual analysis and stochastic likelihood ratio and model probability is proposed. Comparison of these approaches based on the ability to detect, identify and classify faults and the ability to identify faults in spite of system non-linearity. Algorithm is applied to 2 Degrees of Freedom helicopter and the results for various fault cases are presented and the results yield better performance of Interacting Multiple Model based Unscented Kalman Filter.