Correlation Analysis and Augmentation of Samples for a Bidirectional Gate Recurrent Unit Network for the Remaining Useful Life Prediction of Bearings

Abstract

The remaining useful life (RUL) prediction of bearings plays a crucial role in ensuring the safe operation of machinery and reducing costly maintenance. Sufficient degradation information contributes to the accuracy of RUL prediction, but the effective retention of degradation information is still a difficult task, especially for small samples. Thus, a novel RUL prediction method was proposed using data augmentation (DA) and a deep bidirectional gate recurrent unit (DBGRU). Initially, a feature selection method without consideration of the operating time that exploited the mutual information of different units, namely, the main factor correlation index (MFCI), was proposed to construct suitable feature input. In addition, a higher volume of training samples was generated with an effective DA technique, Mixup, and a bidirectional gate recurrent unit (BGRU) layer was utilized to further explore the subtle degradation information with the mixed data and the raw data, which also improved the robustness and generalization of the model. Ultimately, the advantage of the proposed method was demonstrated by a comparison with several state-of-the-art prediction models for the same circumstance. The test results showed that the proposed method was promising and that it had good prediction accuracy and robustness for different working conditions.