Intelligent PHM Based Condition Monitoring in Nuclear Energy Systems

As the critical components age as time goes on, prognostics and health management (PHM) technology have been focused on nuclear energy systems. In this procedure, condition monitoring is vital for determining the turning point (TP) and ensuring an accurate assessment of system operations. This paper proposes a novel condition monitoring method combined with a deep time series-based feature analysis. Firstly, cross-correlation analysis is adopted to make a selection of the transient features related to the key metric. Second, the Piecewise Aggregate Approximation (PAA) is applied to perform further filtering on a time scale. Third, the deep convolutional neural network identifies the TP of a filtered feature in different accidents. Finally, the procedure is integrated with the designed intelligent PHM framework. The experiment results show the superiority of the proposed method. Besides, it has better model integration ability with the developed diagnosis and prediction module.