Remaining useful life prediction via unsupervised domain adaptation considering operation conditions

Abstract

we propose a novel deep learning approach using Unsupervised Domain Adaptation (UDA) that considers physical operation conditions for improving RUL prediction across different domains. Our method modifies the traditional Deep Adversarial Neural Network (DANN) structure by replacing the domain classifier with multiple classifiers. The method was developed and validated based on the new NASA dataset simulated by the Commercial Modular Aero-Propulsion system simulation (N-CMAPSS) with run-to-failure data under realistic flight conditions. The proposed model architecture is compared with the traditional DANN model to demonstrate the goodness of the results and the improvements.