CycleGAN-Based Data Augmentation for Enhanced Remaining Useful Life Prediction under Unsupervised Domain Adaptation

Predictive maintenance is crucial for enhancing operational
efficiency and reducing costs in Prognostics and Health Management
(PHM). One of the key tasks in predictive maintenance
is the estimation of Remaining Useful Life (RUL) of
machinery. In practice, the data for different machines is not
always accessible in sufficient quantity or quality, therefore
the machine learning models trained on machines in one domain
often perform poorly when applied to other domains due
to covariate shifts. As a solution, Domain Adaptation (DA)
aims to tackle domain shifts by extracting domain-invariant
features. However, traditional methods often fail to adequately
address the complexity and variability of real-world
data. We propose to address this challenge, using a Wasserstein
CycleGAN with Gradient Penalty (W-CycleGAN-GP)
to learn mappings between domains and generate augmented
data in the target domain from data in the source domain. We
use our approach to generate realistic augmented data that
bridge domain gap coupled with recent work on adversarialbased
and correlation alignment-based DA models to improve
the performance of RUL prediction models in target domains
without having access to labeled data. The experimental results
on the C-MAPSS dataset demonstrate a significant improvement
in the RUL prediction score and accuracy within
the target domain.