Nuclear power plant components condition monitoring by probabilistic support vector machine

In this paper, an approach for the prediction of the condition of Nuclear Power Plant (NPP) components is proposed, for the purposes of condition monitoring. It builds on a modified version of the Probabilistic Support Vector Regression (PSVR) method, which is based on the Bayesian probabilistic paradigm with a Gaussian prior. Specific techniques are introduced for the tuning of the PSVR hyerparameters, the model identification and the uncertainty analysis. A real case study is considered, regarding the prediction of a drifting process parameter of a NPP component.

► A modified Probabilistic Support Vector Regression (PSVR) method is used in condition monitoring of nuclear power plant.
► Novel and effective hyperparameter tuning and model identification methods are proposed.
► The PSVR-based procedure is effectively used for short-term forecasting of out-of-control parameters.
► A comparison is performed between PSVR and other prediction methods, proving the effectiveness of the former approach.