A Feature Selection Algorithm Based on Variable Correlation and Time Correlation for Predicting Remaining Useful Life of Equipment Using RNN

This In order to make full use of the influence factor of feature changes in the remaining useful life prediction problem of rolling bearings under limited state data, as well as the correlation between the feature and the time, this paper proposes a feature selection method based on variable correlation and time correlation. In this model, MIV (Mean Impact Value) algorithm is used for feature selection at first, which meets the most demands of regression network for the first selection of variables. In addition, the separability measure of residual features is calculated by the correlation coefficient identification, which implements the second feature selection based on time correlation. Then the bearing degradation curve was obtained through RNN (Recurrent Neural Networks). Finally, particle filter is used to obtain the remaining useful life. Experiments show that the feature selection algorithm based on variable correlation and time correlation selects the most informative and sensitive features and it has credibility.