This work studies the problem of fault estimation using a fast adaptive fault diagnosis observer. The estimator bloc considered for this purpose is an Unknown Input Observer (UIO) which is subsequently used for a robust fault detection scheme and also as an adaptive detection design for an additive actuator fault. Stability of the adaptive estimation is provided by a Lyapunov function ending wi...

In this paper, we present an unified approach for fault detection and isolation in discrete-time systems affected by noises and faults on measurement and state equations. The proposed robust fault detection filter will be designed under less restrictive conditions compared with classical fault detection filter. After having parameterized the minimum-time left inverse of the system, the degrees ...

The w orkpresents some simulation results concerning the application of robust model{based fault diagnosis to an industrial process by using iden ti cation and disturbance de{coupling techniques. The rst step of the considered approach iden ti es sev eral equation error models b y means of the input{output data acquired from the monitored system. Each model describes the di erent w orkingcondit...

Detection of faults, such as clogged valves, broken bearings or biased sensors, has beenbrought more and more into focus during the last few decades. There are two mainreasons why faults are important to detect at an early stage. Firstly, faults in safetycritical applications, such as aircraft, nuclear reactors, cars and trains, may create risks ofpersonal injuries. Secondly, fa...

This paper presents a new approach, called robust nonlinear analytic redundancy (RNLAR) technique to actuator fault detection for input-affine nonlinear multivariable dynamic systems. Robust fault detection is important because of the universal existence of model uncertainties and process disturbances in most systems. This paper characterizes the order of redundancy relation for nonlinear syste...

Abstract: This paper addresses the problem of robust identification/fault detection when a model with parametric modelling is considered. Two identification methods are introduced following, respectively, the worst-case and set-membership approaches. In particular those algorithms will use zonotopes to bound the parametric uncertainty. These two identification approaches lead to two robust faul...