Gas turbine gas-path fault identification using nested artificial neural networks

The purpose of this paper is to present a quantitative fault diagnostic technique for a two-shaft gas turbine engine applications.

Nested artificial neural networks (NANNs) were used to estimate the progressive deterioration of single and multiple gas-path components in terms of mass flow rate and isentropic efficiency indices. The data required to train and test this method are attained from a thermodynamic model of the engine under steady-state conditions. To evaluate the tolerance of the method against measurement uncertainties, Gaussian noise values were considered.

The test results revealed that this proposed method is capable of quantifying single, double and triple component faults with a sufficiently high degree of accuracy. Moreover, the authors confirmed that NANNs have derivable advantages over the single structure-based methods available in the public domain, particularly over those designed to perform single and multiple faults together.

This method can be used to assess engine’s health status to schedule its maintenance.

For complicated gas turbine diagnostic problems, the conventional single artificial neural network (ANN) structure-based fault diagnostic technique may not be enough to get robust and accurate results. The diagnostic task can rather be better done if it is divided and shared with multiple neural network structures. The authors thus used seven decentralized ANN structures to assess seven different component fault scenarios, which enhances the fault identification accuracy significantly.