Journal
APPLIED SOFT COMPUTING
Volume 89, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2020.106116
Keywords
Prognostics and health management; Time-series forecasting; Multi-step ahead prediction; Long-short term memory; Nuclear power plant prognostics; Steam generator
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Developing an accurate and reliable multi-step ahead prediction model is a key problem in many Prognostics and Health Management (PHM) applications. Inevitably, the further one attempts to predict into the future, the harder it is to achieve an accurate and stable prediction due to increasing uncertainty and error accumulation. In this paper, we address this problem by proposing a prediction model based on Long Short-Term Memory (LSTM), a deep neural network developed for dealing with the long-term dependencies in time-series data. Our proposed prediction model also tackles two additional issues. Firstly, the hyperparameters of the proposed model are automatically tuned by a Bayesian optimization algorithm, called Tree-structured Parzen Estimator (TPE). Secondly, the proposed model allows assessing the uncertainty on the prediction. To validate the performance of the proposed model, a case study considering steam generator data acquired from different French nuclear power plants (NPPs) is carried out. Alternative prediction models are also considered for comparison purposes. (C) 2020 Elsevier B.V. All rights reserved.
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