Remaining useful life prediction via long-short time memory neural network with novel partial least squares and genetic algorithm |
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Authors: | Ke Yang Yong-jian Wang Yu-nan Yao Shi-dong Fan |
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Affiliation: | 1. School of Energy and Power Engineering, Wuhan University of Technology, Wuhan, China;2. Information Science and Technology, Beijing University of Chemical Technology, Beijing, China |
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Abstract: | Advancements in information technology have made various industrial equipment increasingly sophisticated in recent years. The remaining useful life (RUL) of equipment plays a crucial important role in the industrial process. It is difficult to establish a functional RUL model as it requires the fusion of time-series data across different scales. This paper proposes a long-short term memory neural network, which integrates a novel partial least square based on a genetic algorithm (GAPLS-LSTM). The parameters are first analyzed by PLS to obtain the parameter fusion function of the health index (HI). The GA then searches the optimal coefficients of the function; the expected HI values can be calculated with the fusion function. Finally, the RUL of the equipment is predicted with the LSTM method. The proposed GAPLS-LSTM was applied to RUL prediction of a marine auxiliary engine to validate it by comparison against GAPLS-BP and GAPLS-RNN methods. The results show that the proposed method is capable of effective RUL prediction. |
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Keywords: | health index long-short term memory partial least squares prediction remaining useful life time-series data |
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