Supervised Contrastive Learning based Dual-Mixer Model for Remaining
  Useful Life Prediction
En Fu Yanyan Hu Kaixiang Peng Yuxin Chu
The problem of the Remaining Useful Life (RUL) prediction, aiming at
providing an accurate estimate of the remaining time from the current
predicting moment to the complete failure of the device, has gained significant
attention from researchers in recent years. In this paper, to overcome the
shortcomings of rigid combination for temporal and spatial features in most
existing RUL prediction approaches, a spatial-temporal homogeneous feature
extractor, named Dual-Mixer model, is firstly proposed. Flexible layer-wise
progressive feature fusion is employed to ensure the homogeneity of
spatial-temporal features and enhance the prediction accuracy. Secondly, the
Feature Space Global Relationship Invariance (FSGRI) training method is
introduced based on supervised contrastive learning. This method maintains the
consistency of relationships among sample features with their degradation
patterns during model training, simplifying the subsequently regression task in
the output layer and improving the model's performance in RUL prediction.
Finally, the effectiveness of the proposed method is validated through
comparisons with other latest research works on the C-MAPSS dataset. The
Dual-Mixer model demonstrates superiority across most metrics, while the FSGRI
training method shows an average improvement of 7.00% and 2.41% in RMSE and
MAPE, respectively, for all baseline models. Our experiments and model code are
publicly available at https://github.com/fuen1590/PhmDeepLearningProjects.