Performance diagnostics of gas turbines operating under transient conditions based on dynamic engine model and artificial neural networks

Abstract

Gas turbine engines are machines of high complexity and non-linearity. Interpreting the vast amount of data from a gas turbine and converting them into customer value requires the combination of domain knowledge and modern computational intelligence tools. Reaching to an accurate and reliable diagnosis of gas turbines is a process that is becoming increasingly complex. The engines are now expected to operate in more dynamic conditions to compensate for the intermittent nature of renewables. Transient operating conditions will accelerate the deterioration of gas turbine components which motivates the development of new methods and tools to cope with such type of information. In this paper, we propose a novel performance diagnostic method for gas turbines that combines a dynamic engine model with artificial neural networks (ANN). An engine model of a two shaft gas turbine has been developed in MATLAB/Simulink and used by a family of ANNs to detect the degradation of the engine operating under transient conditions, when all of its components are experiencing degradation. The conducted case studies consider various degradation scenarios. The advantage of the proposed method is that it deals effectively with both fixed and time-evolving degradation. Furthermore, in cases where there is limited amount of data for training ANNs the model can fill this gap by simulating plethora of scenarios that can potentially extend the applicability of ANNs to gas turbine diagnostics. The proposed method could be used as a tool for supporting the operation and maintenance activities of gas turbines.