A Multi-Granularity Supervised Contrastive Framework for Remaining Useful Life Prediction of Aero-engines
This addresses the critical need for accurate RUL predictions to ensure safe aero-engine operation, though it appears incremental as it builds on existing contrastive learning approaches for a specific domain.
The paper tackles the problem of remaining useful life (RUL) prediction for aero-engines by developing a multi-granularity supervised contrastive framework that aligns samples with the same RUL label in feature space, which effectively improves prediction accuracy on the CMPASS dataset.
Accurate remaining useful life (RUL) predictions are critical to the safe operation of aero-engines. Currently, the RUL prediction task is mainly a regression paradigm with only mean square error as the loss function and lacks research on feature space structure, the latter of which has shown excellent performance in a large number of studies. This paper develops a multi-granularity supervised contrastive (MGSC) framework from plain intuition that samples with the same RUL label should be aligned in the feature space, and address the problems of too large minibatch size and unbalanced samples in the implementation. The RUL prediction with MGSC is implemented on using the proposed multi-phase training strategy. This paper also demonstrates a simple and scalable basic network structure and validates the proposed MGSC strategy on the CMPASS dataset using a convolutional long short-term memory network as a baseline, which effectively improves the accuracy of RUL prediction.