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.

本研究提出了一种名为Dual-Mixer模型的时空均匀特征提取器，用于解决大部分现有的RUL预测方法中时空特征刚性组合的缺点。其采用灵活的逐层渐进特征融合来确保时空特征的均匀性并提高预测准确性，同时引入了基于监督对比学习的特征空间全局关系不变性（FSGRI）训练方法来增强模型在RUL预测中的性能。通过与C-MAPSS数据集上的其他最新研究成果进行对比，验证了所提出方法的有效性。Dual-Mixer模型在大多数指标上表现出优越性，而FSGRI训练方法对所有基准模型的RMSE和MAPE平均分别提高了7.00％和2.41％。

基于监督对比学习的双混合器模型用于剩余寿命预测