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Prognostics and Health Management (PHM) is an emerging field which hasreceived much attention from the manufacturing industry because of the benefitsand efficiencies it brings to the table. And Remaining Useful Life (RUL)prediction is at the heart of any PHM system. Most recent data-driven researchdemand substantial volumes of labelled training data before a performant modelcan be trained under the supervised learning paradigm. This is where TransferLearning (TL) and Domain Adaptation (DA) methods step in and make it possiblefor us to generalize a supervised model to other domains with different datadistributions with no labelled data. In this paper, we propose\textit{LAMA-Net}, an encoder-decoder based model (Transformer) with an inducedbottleneck, Latent Alignment using Maximum Mean Discrepancy (MMD) and manifoldlearning is proposed to tackle the problem of Unsupervised Homogeneous DomainAdaptation for RUL prediction. \textit{LAMA-Net} is validated using the C-MAPSSTurbofan Engine dataset by NASA and compared against other state-of-the-arttechniques for DA. The results suggest that the proposed method offers apromising approach to perform domain adaptation in RUL prediction. Code will bemade available once the paper comes out of review.

LAMA-Net: Unsupervised Domain Adaptation via Latent Alignment and Manifold Learning for RUL Prediction