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Remaining useful life prediction via long‐short time memory neural network with novel partial least squares and genetic algorithm
Author(s) -
Yang Ke,
Wang Yongjian,
Yao Yunan,
Fan Shidong
Publication year - 2021
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2782
Subject(s) - artificial neural network , genetic algorithm , computer science , function (biology) , process (computing) , series (stratigraphy) , time series , algorithm , long short term memory , data mining , partial least squares regression , fusion , artificial intelligence , machine learning , recurrent neural network , paleontology , linguistics , philosophy , evolutionary biology , biology , operating system
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.