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Arxiv
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A Multi-Granularity Supervised Contrastive Framework for Remaining Useful Life Prediction of Aero-engines
- Accurate remaining useful life (RUL) predictions are crucial for safe operation of aero-engines.
- This paper introduces a multi-granularity supervised contrastive (MGSC) framework to address limitations in current RUL prediction methods.
- The MGSC framework aims to align samples with the same RUL label in the feature space, improving prediction accuracy.
- The proposed strategy is implemented on the CMPASS dataset and enhances RUL prediction accuracy using a convolutional long short-term memory network as a baseline.
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