A proposal for the diagnosis of uncertain dynamic systems based on interval models

The performance of a model-based diagnosis system could be affected by several uncertainty sources, such as, model errors, uncertainty in measurements, and disturbances. This uncertainty can be handled by mean of interval models. The aim of this thesis is to propose a methodology for fault detection, isolation and identification based on interval models. Especially a modal interval based technique, and interval based consistency technique (ICTs) are considered. The thesis includes some proposals to perform each stage within the diagnosis process, from the design of the fault detection tests, to the quantitative fault isolation and identification stage. Thus, the methodology includes some algorithms to obtain in an automatic way the symbolic expression of the residual generators enhancing the structural isolability of the faults, in order to design the fault detection tests. These algorithms are based on the structural model of the system. The stages of fault detection, isolation, and identification are stated as constraint satisfaction problems in continuos domains and solved by means of interval based consistency techniques. The qualitative fault isolation is enhanced by a reasoning in which the signs of the symptoms are derived from analytical redundancy relations or bond graph models of the system. The quantitative fault isolation and identification is performed by means of a faulty parameters estimation approach. An initial and empirical analysis regarding the diferencies between interval-based and statistical-based techniques is presented in this thesis. Methods included in this comparison are, ICTs on one side, and a statistical decision technique that combines Extended Kalman filter and Z-test on the other side. The performance and efficiency of the contributions are illustrated through several application examples, covering different levels of complexity.