Prof. Yun's paper "Generative Diffusion Model-Based Deep Learning Framework for Remaining Useful Life Prediction" got accepted to IEEE Internet of Things Journal (IF 8.2, JCR Q1 Rank 3.4%)!
In this letter, we propose a novel and high-performing deep learning framework for remaining useful life (RUL) prediction, called RUL-Diff, by leveraging a generative diffusion model. It is composed of two modules that are connected in tandem: 1) a feature extractor corresponding to the encoder part of our customized U-Net and 2) a RUL predictor constructed by a multilayer perceptron. We further devise an effective two-stage training methodology for the proposed RUL-Diff, in which the feature extractor is initially pretrained for high-quality feature learning, and then, is retrained jointly with the RUL predictor for accurate RUL prediction. Extensive experimental results on NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) datasets demonstrate the superiority and effectiveness of the proposed scheme.
