Residual life prediction of rolling bearing based on attention GRU algorithm

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Journal of Vibration and Shock ›› 2021, Vol. 40 ›› Issue (17) : 116-123.

YAO Dechen1,2, LI Boyang1,2, LIU Hengchang1,2, YAO Juanjuan3, PI Yannan3

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Abstract

Aiming at the problem of it being difficult to accurately predict the change trend of rolling bearing residual life with time variation in rotating machinery, making full use of the ability of recurrent neural network (RNN) to process time series data, the attention gated recurrent unit (AGRU) algorithm based on attention mechanism was proposed to predict rolling bearing residual life.Firstly, multiple time-domain features were extracted from original vibration signals to construct a data set, and the data set was normalized.Secondly, the attention mechanism was introduced into GRU model.Finally, the feature data set was divided into a training set and a testing set.The training set was used to train the model and determine the optimal model parameters.The testing set was used to evaluate the effect of the model.The experimental results showed that the improved GRU model can effectively predict the change trend of residual life of different types of rolling bearing with time variation; it can provide a new idea for predicting residual life of rolling bearing components.

Key words

rolling bearing / feature data set / gated recurrent unit (GRU) algorithm / attention mechanism / life prediction

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YAO Dechen, LI Boyang, LIU Hengchang, YAO Juanjuan, PI Yannan. Residual life prediction of rolling bearing based on attention GRU algorithm[J]. Journal of Vibration and Shock, 2021, 40(17): 116-123

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