Formal model for real time diagnosis of dynamic systems

The task of real-time causal diagnosis of disturbances has been conceived traditionally from a procedural point of view, in the sense that the attention is focused on developing efficient procedures capable of evaluating the state of some variables of the system so that real time objectives imposed were satisfied. The main handicap of these methods lies in the difficulty to plan the diagnostic process, particularly when a high number of variables are to be observed model-based diagnosis constitutes a more complete approach to the topic. Considering the availability of a simulated model of the system, the task of the diagnostic procedure is now performed on the simulated model, facilitating the observation and handling of the variables of the system. However, the absence of languages allowing us to develop simulated models of real systems limits the use of this theory to simple cases. An approach to real-time causal diagnosis of dynamic systems based on a pre-established planning of any possible diagnostic situation in such a way real-time objectives are satisfied, is presented in this work. Artificial intelligence techniques, particularly inductive methods have been considered according to two essential steps: formulation of the causal diagnostic model, specifying the particular characteristics of the problem in hand; and generation of an information structure according to the characteristics of the formulated model, whose performance will guarantee the diagnostic objectives.