Pattern Classification: Neuro-Fuzzy Methods and Their Comparison
Pattern Classification: Neuro-Fuzzy Methods and Their Comparison
| Hi-index | 0.00 |
Fault detection and isolation (FDI) has become a useful strategy for determining fault appearance and on-line reconfiguration. However, unknown scenarios during on-line performance are still an open field for research. Different methods, such as knowledge-based techniques or analytical redundancy, have been followed. Nevertheless, both methods present inherent drawbacks for isolation. The present paper introduces a combined approach of model- and knowledge-based methods, using an autonomous element for isolation of unknown scenarios during on-line stage. The contribution is to integrate both methods to accomplish fault localization for unknown scenarios, based on previous information. Faults are constrained to certain bounded frequency response.