Causality-Preserving Domain Generalization via Adaptive Fourier Mixup for RUL Prediction

This paper presents AFM-CIR, a framework designed to help AI models predict equipment failure times (RUL prediction) even when trained on data from one environment but tested on different environments (domain generalization). The approach combines Adaptive Fourier Mixing (a technique that blends training data in frequency space while preserving causal relationships) with Causality-Inspired Regression to create training data that works across different domains, and experiments show it outperforms existing methods.