Simplified Data-Driven Models for Gas Turbine Diagnostics

The maintenance of gas turbines relies a lot on gas path diagnostics (GPD), which includes two approaches. The first approach employs a physics-based model (thermodynamic model) to convert measurement shifts (deviations) induced by deterioration into fault parameters, which drastically simplify diagnostics. The second approach relies on data-driven models, makes diagnosis in the space of measurement deviations, and involves pattern recognition techniques. Although a thermodynamic model is an essential element of GPD, it has limitations. This model is a complex software critical to computer resources, and the computation sometimes does not converge. Therefore, it is difficult to use the model in online applications. Since the 1990s, we have developed many thermodynamic models for different engines. Since the 2000s, simplified data-driven models were investigated. This paper proposes to substitute a thermodynamic model for novel simplified data-driven models that have the same functionality, i.e., take into consideration the influence of both operating conditions and engine faults. The proposed models are formed and compared with the underlying thermodynamic model. To obtain a solid conclusion about these models, they are verified in twelve test cases formed by three test-case engines, two model types, and two approximation functions. Although the accuracy of the simplified models varies from 1.15% to 0.0082%, it was found acceptable even for the worst case. Thus, these simple-but-accurate models with the functionality of a physics-based model represent a good replacement for the latter. It is expected that the models will stimulate the further development of advanced diagnostic systems.