Robust fault diagnosis of state and sensor faults in nonlinear multivariable systems

The paper presents a robust fault diagnosis scheme for detecting and approximating state and sensor faults occurring in a class of nonlinear multi-input multi-output systems. The changes in the system dynamics due to a fault are modeled as nonlinear functions of the control input and measured output variables. Both state and sensor faults can be modeled as slowly developing (incipient) or abrupt, with each component of the state/sensor fault vector being represented by a separate time profile. The robust fault diagnosis scheme utilizes online approximators and adaptive nonlinear filtering techniques to obtain estimates of the fault functions. Robustness, fault sensitivity and stability conditions of the learning scheme are rigorously derived.