Nested joint fault detection, identification, estimation, and state estimation

Abstract

A fault detection, identification, estimation and state estimation (FDIESE) problem involves joint decision and estimation (JDE). Decision contains detection and identification, while estimation is for fault severeness and system state. Both detection and identification are highly coupled with estimation and a fault is identified after detection. To solve this problem, an approach named nested joint FDIESE (NJFDIESE) is proposed. It considers detection and state estimation jointly first, and then does identification and fault severeness estimation jointly given the detection. NJFDIESE addresses adequately the coupling among detection, identification and estimation. Moreover, to estimate the fault severeness, which is modeled as a bounded continuous-valued random variable, a variable-structure interacting multiple-model estimator is proposed in the NJFDIESE framework. To evaluate the proposed algorithm, results of a simulation study of a flight control system with sequential actuator failures are presented. They show that the NJFDIESE outperforms the decision then estimation, the separate estimation and decision, and the existing joint FDIESE methods in joint performance.