Dual Mix-up Adversarial Domain Adaptation for Machine Remaining Useful Life Prediction

Abstract

Remaining useful life (RUL) prediction is one of the core issues in the equipment maintenance process. It aims to accurately forecast machines’ run-to-failure life span using previous and current state data. As various data-driven models are proving to be effective, because RUL labels for machines in particular conditions are difficult to obtain, domain adaptation approaches begins to be explored in the RUL prediction issue. We propose a novel Dual Mix-up Adversarial Domain Adaptation (DMADA) approach to further improve the RUL forecasting accuracy, building on existing RUL domain adaptation studies. In DMADA, both time-series mix-up and domain mix-up regularization are conducted. Virtual samples generated by linear interpolation lead to enriched and more continuous sample space. The linear interpolation encourages consistency of prediction in-between samples and allows the model to explore the feature space more thoroughly. At the same time, the domain mixup conserves the invariance of learned features. The two mixup regularizations combined promote both the transferability and the discriminability of extracted features, which is essential to satisfactory unsupervised domain adaptation performance. Thorough experiments on the C-MPASS dataset are conducted and satisfactory results prove the proposed approach effective.