Remaining useful life prediction techniques for electric valves based on convolution auto encoder and long short term memory

To optimize the operation and maintenance of nuclear power systems, this study presents a remaining useful life (RUL) prediction method for electric valves by combining convolutional auto-encoder (CAE) and long short term memory (LSTM). CAE can extract deeper features and LSTM is efficient in dealing with time-series data. Moreover, by designing a parallel structure between the outputs of CAE and the original data, features fed into the LSTM are enriched. Also, network structure and corresponding hyper-parameters are compared to obtain a more suitable model. Moreover, the accuracy of the proposed method is tested and compared with other machine learning algorithms. This work also serves as a critical innovation to enhance the safety and economic operation of nuclear plants and other complex systems. (c) 2020 ISA. Published by Elsevier Ltd. All rights reserved.

Automated summary

This study proposes a method for predicting the remaining useful life of electric valves by combining CAE and LSTM technologies, comparing network structures and hyperparameters to obtain a more suitable model, and testing the accuracy of the method to enhance the safety and economic operation of nuclear plants and other complex systems.