Fault Detection and Isolation of Three-tank System using Neurofuzzy Networks with Local Approaches
نویسنده
چکیده
In this paper, a fault detection and isolation (FDI) scheme is derived based on fuzzy rules extracted from the neurofuzzy network that models the residual of the system. First, a fault database (FDB) is constructed from fuzzy rules extracted from the neurofuzzy networks that model all possible faults in the system. By comparing the currently extracted fuzzy rules with those in the FDB using the nearest neighbour classifier, faults are diagnosed online. As the number of rules in the FDB can be quite large, the FDI scheme proposed here utilises the local approaches to reduce the computation load and to improve the sensitivity of the method. The proposed FDI scheme is successfully applied to diagnose faults in a nonlinear three-tank control system.
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