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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.

High-speed water jets are widely used in deep mining and the in-depth study of jet characteristics helps to improve drilling efficiency. Three-dimensional Large Eddy Simulation is used to simulate turbulent flows generated by an organ-pipe nozzle. The simulation is validated with existing experimental data and is focused on the evolution and interaction of cavitation bubbles and vortices. Dynamic mode decomposition is performed to extract structural information about the different motion modes and their stability. Results show that the dominant fluid frequency is positively correlated with inlet pressure while unrelated to the divergence angle. Meanwhile, jets’ oscillation is amplified by a large divergence angle, which facilitates the occurrence of cavitation. Results about the flow field outside of an organ-pipe nozzle advance the understanding of the basic mechanism of cavitation jets.

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