Sensors are highly essential components in most modern control systems and are used in increasingly complex ways to improve system precision and reliability. Since they are generally susceptible to faults it is common to perform on-line fault diagnostics on sensor data to verify nominal behavior. This is especially important for safety critical systems where it can be imperative to identify, and react to, a fault before it increases in severity. An example of such a safety critical system is the propulsion control of a vehicle. In this thesis, three different model-based methods for Fault Detection and Isolation (FDI) are developed and tested with the aim of detecting and isolating sensor faults in the powertrain of an electric, center articulated, four-wheel-drive vehicle. First, kinematic models are derived that combine sensor data from all sensors related to propulsion. Second, the kinematic models are implemented in system observers to produce fault sensitive zero-mean residuals. Finally, fault isolation algorithms are derived, which detect and indicate different types of faults via evaluation of the observer residuals. The results show that all FDI methods can detect and isolate stochastic faults with high certainty, but that offset-type faults are hard to distinguish from modeling errors and are therefore easily attenuated by the system observers. Faults in accelerometer sensors need extra measures to be detectable, owing to the environment where the vehicle is typically operated. A nonlinear system model shows good conformity to the vehicle system, lending confidence to its further use as a driver for propulsion control.

Cette thèse traite du problème d'observation et d'estimation des variables caractéristiques des systèmes dynamiques. Il s’agit d’une problématique fondamentale qui est au cœur de nombreux domaines relavant des sciences de l'ingénieur. Les travaux sont conduits dans un contexte ensembliste. Les techniques développées pour l’estimation de l’état et des variables d’entrées ont pour objectif final le contrôle de cohérence des systèmes non linéaires à temps continu. Une première approche conjugue les relations de parité et les différentiateurs à modes glissants pour l’estimation des entrées d’un système non linéaire. Les domaines des entrées compatibles avec les mesures sont alors reconstruits grâce à l’analyse par intervalles et aux techniques de satisfaction de contraintes. Il est montré que la relaxation des contraintes de stabilité/coopérativité pour la construction d’un observateur intervalle peut se faire grâce à des changements de base déterminés de différentes manières et pouvant être variants ou invariants dans le temps. Des simulations numériques illustrent les techniques proposées. Une application à un système aéronautique est également présentée à l’aide d’un jeu de données réelles. / This thesis deals with the problem of a dynamical system observation and the estimation of its characteristic variables; the latter point constitutes the core element in many engineering science fields. The final aim is to build a general framework for integrity control and fault detection of such systems within a bounded error context. The developments offered herein make use of parity relations, sliding mode differentiators, interval observers and constraint satisfaction problems. Input reconstruction techniques are developed for a general class of nonlinear continuous-time systems. Domains are reconstructed for the input values which are consistent with the measurements using interval analysis and constraint satisfaction techniques. It is shown that time-varying or invariant coordinate changes may relax the applicability conditions (stability/cooperativity) of the interval observer design methods. Sliding mode differentiators were also used to enhance interval observer accuracy. The proposed approaches are illustrated through computer simulations and they have been applied to aircraft servo loop control surface for robust and early detection of abnormal positions.