LSTM networks based on attention ordered neurons for gear remaining life prediction

Abstract

Gear is a commonly-used rotating part in industry, it is of great significance to predict its failure in advance, which is helpful to maintain the health of the whole machine. Firstly, the isometric mapping algorithm is applied to construct the health indicator (HI) based on the statistical characteristics of gear. Then a novel variant of long-short-term memory neural network with attention-guided ordered neurons (LSTM-AON) is constructed to achieve the accurate prediction of gear remaining useful life (RUL). LSTM-AON divides the hierarchy of health characteristic information via attention ordered neurons, so that it can use the sequence information of neurons to improve the predictive performance, which improves the long-term prediction ability and robustness. The experiments show the superiority of the new gear RUL prediction methodology based on LSTM-AON compared to the current prediction methods.

Keywords: Attention mechanism; Data-driven; Life cycle data; Ordered neurons; RUL prediction.