A Data-Driven Approach for Predicting the Remaining Useful Life of Steam Generators

Abstract

The reliability of steam generators in nuclear power plants has always been a challenging issue. Various diagnostic models have been proposed in the literature. However, no work has been reported on the development of a robust prediction model for forecasting the future health state of steam generators. In this paper, we propose an ARIMA-based prognostic approach for tracking the degradation evolution in a steam generator and further predicting its Remaining Useful Life (RUL) before breakdown. A case study concerning real degradation datasets from different steam generators is extensively investigated to validate the performance of the proposed model.