TY - JOUR N2 - The paper focuses on the problem of robust fault detection using analytical methods and soft computing. Taking into account the model-based approach to Fault Detection and Isolation (FDI), possible applications of analytical models, and first of all observers with unknown inputs, are considered. The main objective is to show how to employ the bounded-error approach to determine the uncertainty of soft computing models (neural networks and neuro-fuzzy networks). It is shown that based on soft computing models uncertainty defined as a confidence range for the model output, adaptive thresholds can be described. The paper contains a numerical example that illustrates the effectiveness of the proposed approach for increasing the reliability of fault detection. A comprehensive simulation study regarding the DAMADICS benchmark problem is performed in the final part. L1 - http://www.czasopisma.pan.pl/Content/111692/PDF-MASTER/(54-1)75.pdf L2 - http://www.czasopisma.pan.pl/Content/111692 PY - 2006 IS - No 1 EP - 88 KW - fault detection KW - robustness KW - unknown input observer KW - neural networks KW - neuro-fuzzy systems KW - bounded-error approach KW - model uncertainty A1 - Korbicz, J. VL - vol. 54 DA - 2006 T1 - Robust fault detection using analytical and soft computing methods SP - 75 UR - http://www.czasopisma.pan.pl/dlibra/publication/edition/111692 T2 - Bulletin of the Polish Academy of Sciences Technical Sciences ER -