On fault detection under soft computing model uncertainty
نویسندگان
چکیده
The paper deals with the problems of robust fault detection using soft computing techniques, in particular neural networks (Group Method of Data Handling, GMDH), multi-layer perceptron), and neuro-fuzzy networks (Takagi-Sugeno model). The model based approach to Fault Detection and Isolation (FDI) is considered. The main objective is to show how to employ the bounded-error approach to determine the uncertainty of the neural and fuzzy models. It is shown that, based on soft computing models uncertainty defined as a confidence range for the model output, adaptive thresholds can be defined. Finally, the presented approaches are tested on a servoactuator being an FDI benchmark in the DAMADICS project. Copyright c ©2007 IFAC
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تاریخ انتشار 2008