This paper presents several innovations in the development of model-based diagnostic systems for diagnosing faults in continuous dynamic physical systems. The approach utilises recent developments in qualitative simulation techniques to cope with the inherent lack of modelling knowledge and to provide a qualitative description of the dynamic behaviour. In particular, techniques for the synchronous tracking of the model-based predictions and the evolution of the physical system between equilibria are developed. A discrepancy metric is defined that allows for the continuous degradation of the system behaviour from normal to faulty to be detected. And, most fundamentally, a method for iteratively searching through the space of possible model variations is presented. This provides explicit feedback from detected discrepancies to model adjustments and has the important advantage of reducing the sensitivity to modelling errors and approximate fault models. In the limit, no fault models are required. However, if available these can be used to initialise the search. An example is included which outlines the basic approach discussed in this paper. © 1995.
|Number of pages||19|
|Journal||Artificial Intelligence in Engineering|
|Publication status||Published - 1995|
- fuzzy sets
- model-based diagnosis
- qualitative modelling
- qualitative simulation