Large-scale complex process plants are safety-critical systems where the real-time diagnosis is very important. In a model based systems engineering approach, the structured development process from the concept to the production to the operation phase is organized around a coherent model of the system. This model contains, in particular, relations about the behavior of the system that could have been used for simulation in the design phase. The objective of this work is to use this information to design automatically on-line diagnosis algorithms using the hybrid dynamical information part and sensor measurements of the system model. In this thesis, the proposed approach allows:To extract the valid relations of system behavior to take into account system evolution by eliminating invalid constraints and measurements for establishing an on-line diagnosisTo build, using symbolic analysis and graph path search, analytical redundancy relations for the various system configurationsTo evaluate these ARRs in using set valued computations (interval arithmetic) to take into account model and measurements uncertainties
