Robust identification and residual generation application to a turbofan engine

We describe an application of a model-based fault detection and isolation technique to a turbofan aircraft engine. Our approach is based in H_∞-optimization, a frequency domain technique that explicitly addresses robustness. The engine model is obtained through a robust identification approach combining the classical time-data system identification via prediction error models and the so-called model error model theory. The time-data series are provided by one of Honeywell´s airline customers. The model error model approach provides guidelines for the characterization of the model uncertainty stemming from the nominal system identification. The H_∞ filter is used to detect faults in the engine´s high-pressure turbine while minimizing disturbances and commands effects.