A Novel Combination Neural Network Based on ConvLSTM-Transformer for Bearing Remaining Useful Life Prediction

A sensible maintenance strategy must take into account the remaining usable life (RUL) estimation to maximize equipment utilization and avoid costly unexpected breakdowns. In view of some inherent drawbacks of traditional CNN and LSTM-based RUL prognostics models, a novel combination model of the ConvLSTM and the Transformer, which is based on the idea of “Extracting spatiotemporal features and applying them to RUL prediction”, is proposed for RUL prediction. The ConvLSTM network can directly extract low-dimensional spatiotemporal features from long-time degradation signals. The Transformer, based entirely on attention mechanisms, can deeply explore the mapping law between deep-level nonlinear spatiotemporal feature information and equipment service performance degradation. The proposed approach is validated with the whole-life degradation dataset of bearings from the PHM 2012 Challenge dataset and the XJTU-SY public dataset. The detailed comparative analysis shows that the proposed method has higher RUL prediction accuracy and outstanding comprehensive prediction performance.