Gas turbine industry currently implements prognostic and health management systems as a fundamental task to predict the deteriorated characteristics of a gas turbine at future states and in turn plan maintenance actions. Thus, economic losses caused by system breakdowns and unnecessary repair actions can be reduced.
In this work, a data-driven Bayesian Hierarchical Model (BHM) is implemented by means of an innovative autoregressive structure to predict gas turbine progressive deterioration. The novel autoregressive model provides an estimate of the output variable which depends on time and its previous values.
In such a model, lagged values of the output are used as predictor variables. The autoregressive BHM, called ARBHM in this paper, is applied to highly heterogeneous field data taken from the literature, characterized by different degradation rates and referred to the power output of a large-size heavy duty gas turbine.
The ARBHM tested in this paper includes up to a third-order lag and is compared to a BHM that only uses time as the regression variable. The comparison is carried out by performing both single-step prediction and multi-step prediction of power output. The results demonstrate that, in the considered degradation scenarios, the innovative ARBHM is usually preferable to BHM, since prediction errors decrease up to 2.0 % in the best case.