This paper describes a new approach to the development of a fault diagnostics and prognostic capability for an advanced cycle gas turbine. It is based on techniques using sensor based and model based information. Sensor based information is the actual information obtained from the real engine and the model based information comes from the data obtained from engine performance model simulation with a permutation of implanted faults taking into account sensor noise and bias. The approach adopted here is to minimize an objective function which represents the difference between the actual and simulated data and the minimized objective function allows us identify the nature of fault. After the initial success with simple cycle engines, it was decided to extend this technique to advanced cycle engines. The technique is being tested on an in-house model of an intercooled recuperated engine with variable geometry similar to the ICR-WR21cycle. A detailed analysis of the technique applied to simple cycle and advanced cycle will be presented.

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