A hybrid model based on complementary ensemble empirical mode decomposition, sample entropy and long short-term memory neural network for the prediction of time series signals in NPPs

Abstract

Accurate and reliable predictions are fundamental for the condition monitoring and maintenance of components and systems in nuclear power plants (NPPs). In this work, we propose a hybrid condition prediction approach based on the combination of complementary ensemble empirical mode decomposition (CEEMD), sample entropy (SampEn) and optimized long short-term memory (LSTM) neural network. Firstly, the CEEMD decomposes the time series signals into multiple subsequences called intrinsic mode functions (IMFs), by doing so, the complexity of the time series signals can be reduced, and this facilitates the accurate prediction of the original signals. Then, in order to reduce the calculation cost of prediction models for subsequences, SampEn is used to measure the complexities of the IMFs, and the IMFs whose values of SampEn are lower than the average are aggregated into a new component. Finally, the LSTM with the hyperparameters optimized by the Bayesian optimization algorithm (BOA) is used to perform the prediction of each component. The prediction results of the original signals are reconstructed by synthesizing the predictions of all components. The proposed hybrid prediction model is utilized on the time series signals collected from an NPP. The results obtained show that the proposed approach can capture the characteristics of the signals and has better performance in prediction accuracy than other models.