NONLINEAR SURROGATE MODELS FOR GAS TURBINE DIAGNOSIS

In gas turbine diagnostics, a significant contradiction is observed between the variety of methods proposed and a limited number of the algorithms realized in real monitoring systems. One of the explanations is the shortage of simple, but reliable and accurate diagnostic solutions. Many actual solutions are based on a nonlinear thermodynamic model; however, this physics-based model and an iterative procedure to adapt it are complex, critical to computer resources, and does not always converge. On the other hand, the used linear models are simple, but not accurate enough. The present paper proposes and analyzes two types of nonlinear simplified static data-driven models based on the steady-state data generated by the thermodynamic model. The first type includes direct models that compute monitored variables Y for given operating conditions U and fault parameters δΘ. The second type presents inverse models that estimate parameters δΘ using variables U and Y as inputs. The paper aims to create such models, optimize them, and show that they can be a good surrogate for the original thermodynamic model and its adaption procedure. Based on the experience of the development of baseline models, we employ polynomials and multilayer perceptron as approximation techniques. Adjustment of the techniques and their comparison allow choosing the best one. To draw solid conclusions on the utility of the proposed models, three different engines are used as test cases. The results of the verification of these models are promising for their use in gas turbine diagnostic and control systems.