Application of Forecasting Methodologies to Predict Gas Turbine Behavior Over Time

Abstract

The growing need to increase the competitiveness of industrial systems continuously requires a reduction of maintenance costs, without compromising safe plant operation. Therefore, forecasting the future behavior of a system allows planning maintenance actions and saving costs, because unexpected stops can be avoided. In this paper, four different methodologies are applied to predict gas turbine behavior over time: Linear and Nonlinear Regression, One Parameter Double Exponential Smoothing, Kalman Filter and Bayesian Forecasting Method. The four methodologies are used to provide a prediction of the time when a threshold value will be exceeded in the future, as a function of the current trend of the considered parameter. The application considers different scenarios which may be representative of the trend over time of some significant parameters for gas turbines. Moreover, the Bayesian Forecasting Method, which allows the detection of discontinuities in time series, is also tested for predicting system behavior after two consecutive trends. The results presented in this paper aim to select the most suitable methodology that allows both trending and forecasting as a function of data trend over time, in order to predict time evolution of gas turbine characteristic parameters and to provide an estimate of the occurrence of a failure.