This paper presents an investigation of a conventional gas turbine diagnostic process and its generalization. A usual sequence of diagnostic actions consists of two stages: monitoring (fault detection) and subsequent proper diagnosis (fault identification). Such an approach neither implies fault identification nor uses the information about incipient faults unless the engine is recognized as faulty. In previous investigations for engine steady state operation conditions we addressed diagnostics problems without their relation with the monitoring process. Fault classes were given by samples of patterns generated by a static gas turbine performance model. This fault simulation took into account faults of varying severity including incipient ones. A diagnostic algorithm employed artificial neural networks to identify an actual fault. In the present paper we consider the monitoring and diagnosis as joint processes extending our previous approach over both of them. It is proposed to form two classes for the monitoring using the above-mentioned classes constructed for the diagnosis. A two-shaft industrial gas turbine has been chosen to test the proposed integrated approach to monitoring and diagnosis. A general recommendation following from the presented investigation is to identify faults simultaneously with fault detection. This permits accumulating preliminary diagnoses before the engine faulty condition is detected and a rapid final diagnosis after the fault detection.

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