A generalized fault classification for gas turbine diagnostics on steady states and transients

Gas turbine diagnostic techniques are often based on the recognition methods using the deviations between actual and expected thermodynamic performances. The problem is that the deviations depend on real operating conditions. However, our studies show that such a dependency can be reduced. In this paper, we propose the generalized fault classification that is independent of the operating conditions. To prove this idea, the averaged probabilities of the correct diagnosis are computed and compared for two cases: the proposed classification and the traditional one based on the fixed operating conditions. The probabilities are calculated through a stochastic modeling of the diagnostic process, in which a thermodynamic model generates deviations that are induced by the faults. Artificial neural networks recognize these faults. The proposed classification principle has been realized for both, steady state and transient operation of the gas turbine units. The results show that the acceptance of the generalized classification practically does not reduce the diagnosis trustworthiness.