Gas Turbine Engine and Sensor Fault Diagnosis Using Optimization Techniques

Abstract

A diagnostic system is described for performance analysis of gas turbine engine components and sensors. The system estimates the performance parameters expressing the fault condition of the engine components in the presence of measurement noise and biases. The measurement uncertainty is supposed to affect even the parameters setting the operating condition of the engine. Estimation is performed through bptimization of an objective function by means of an ad hoc genetic algorithm. The genetic algorithm uses an accurate nonlinear steady-state performance model of the engine. The only statistical assumption required by the technique concerns the measurement noise and the maximum allowed number of faulty sensors and engine components, which is enclosed as a constraint. The technique has been thoroughly tested with the model of a low bypass ratio turbofan, and the results show the high level of accuracy achieved.