Nonlinear model-based fault detection with fuzzy set fault isolation

This paper presents a nonlinear fault detection and isolation system that is able to distinguish single faults that have the same fault signatures. The detection mechanism is based on nonlinear state estimation. Fuzzy set theory followed by parameter estimation of certain parameters of the fault-free model are applied for fault isolation. This parameter estimation step is used to differentiate between a variety of faults, including those with similar signatures. The proposed fault detection and isolation (FDI) method is validated using an air heater lab experiment. Actuator and sensor faults are considered and comparisons with other methods are presented and analyzed under different fault scenarios. The proposed FDI method shows significant advantages when it is applied to nonlinear model systems with fault-free models available.