Remaining useful life prediction via long-short time memory neural network with novel partial least squares and genetic algorithm

作者：Yang, Ke; Wang, Yong-jian; Yao, Yu-nan; Fan, Shi-dong^*

来源：QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, 2021, 37(3): 1080-1098.

DOI：10.1002/qre.2782

• health index
• long-short term memory
• partial least squares
• prediction
• remaining useful life
• time-series data

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.

• 单位
  北京化工大学; 武汉理工大学