Fault Detection and Isolation of Industrial Processes Using Optimized Fuzzy Models

Model-based fault detection and diagnosis is an interesting method, due to economical and safety related matters. However, in practice it is very difficult to achieve accurate models for complex nonlinear plants. If the plant structure is not precisely known, the diagnosis has to be based primarily on data or heuristic information. The inherent characteristics of fuzzy logic theory make it suitable for fault detection and isolation (FDI). In this paper is proposed a modification to the regularity criterion algorithm, aiming the reduction of computational time without loss of accuracy. The fuzzy models obtained using the modified regularity criterion algorithms are optimized by a real-coded genetic algorithm. An industrial valve simulator is used to obtain several abrupt and incipient faults in the system. These faults are some of the possible faults in the real system. The fuzzy models used in the FDI system were able to detect and isolate the simulated abrupt and incipient faults