Passive Robust Fault Detection of Dynamic Processes Using Interval Models

Model-based fault detection relies on the use of a model to check the consistency between the predicted and the measured (or observed) behavior of a system. However, there is always some mismatch between the modeled and the real process behavior. Then, any model-based fault detection algorithm should be robust against modeling errors. One possible approach to take into account modeling uncertainty is to include all the uncertainty in system parameters using an interval model that allows generating an adaptive threshold. In this paper, the use of interval models in robust fault detection considering three schemes (simulation, prediction, or observation) is presented and discussed. The main contribution is to present a comparative study that allows identifying the benefits and drawbacks of using each scheme. This study will provide a guideline for the use of the proposed fault detection schemes in real applications. Finally, an intelligent servoactuator, proposed as a benchmark in the context of European Research Training Network DAMADICS, is used to illustrate the application and the comparative study of these interval-based fault detection schemes.