Abstract
A novel approach to time constrained model-based fault diagnosis of ill-defined dynamic systems is presented. A fuzzy qualitative simulation algorithm is used to generate qualitative predictions of the dynamic behaviour of the faulty process. The predictions are then compared to the observed behaviour to identify the correct parameter values of the qualitative model. By comparing these identified parameter values to their known fault-free or nominal values, faults in the process can be detected and identified. Further, a control module for this qualitative parameter identification system is developed, which can trade-off aspects of the solution quality for time. In particular, the precision of the model and the completeness of the qualitative simulation can be traded-off to enable a solution to be produced within a user prescribed time constraint. Experimental results for a benchmark 3rd order dynamic system are given. © 1997 Elsevier Science Ltd. All rights reserved.
| Original language | English |
|---|---|
| Pages (from-to) | 417-427 |
| Number of pages | 11 |
| Journal | Engineering Applications of Artificial Intelligence |
| Volume | 10 |
| Issue number | 5 |
| Publication status | Published - Oct 1997 |
Keywords
- Artificial intelligence
- Fault diagnosis
- Fuzzy models
- Parameter identification
- Qualitative simulation
- Real-time AI