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.

中文翻译：

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通过具有新颖的偏最小二乘和遗传算法的长时记忆神经网络进行剩余寿命预测

近年来，信息技术的进步使各种工业设备变得越来越复杂。设备的剩余使用寿命（RUL）在工业过程中起着至关重要的重要作用。建立功能性RUL模型非常困难，因为它需要融合不同规模的时间序列数据。本文提出了一种长期短期记忆神经网络，该网络集成了基于遗传算法（GAPLS-LSTM）的新颖的偏最小二乘。首先通过PLS分析参数以获得健康指数（HI）的参数融合函数。遗传算法然后搜索函数的最佳系数；可以使用融合函数计算出预期的HI值。最后，使用LSTM方法预测设备的RUL。拟议中的GAPLS-LSTM通过与GAPLS-BP和GAPLS-RNN方法的比较，应用于船舶辅助发动机的RUL预测中，以对其进行验证。结果表明，该方法能够有效地进行RUL预测。