论文标题
喇嘛网络：通过潜在对齐和多种统治预测，无监督的领域适应
LAMA-Net: Unsupervised Domain Adaptation via Latent Alignment and Manifold Learning for RUL Prediction
论文作者
论文摘要
预后和健康管理（PHM）是一个新兴领域，由于其带来的好处和效率，它引起了制造业的广泛关注。剩余的使用寿命（RUR）预测是任何PHM系统的核心。最新数据驱动的研究要求在有监督的学习范式下对表现模型进行培训之前，大量标记的培训数据。在这里，转移学习（TL）和域适应（DA）方法介入并使我们有可能将监督模型概括为具有不同数据分布的其他没有标记数据的域。在本文中，我们提出了一种基于编码器的模型（变压器），该模型（变压器）具有诱导的瓶颈，使用最大平均差异（MMD）的潜在对准，并提出了流形学习，以解决无监督的同质域的问题的问题，以实现统治预测。 \ textit {lama-net}使用NASA使用C-Mapss Turbofan引擎数据集验证，并将其与DA的其他最新技术进行了比较。结果表明，所提出的方法提供了一种有希望的方法来在RUL预测中进行域的适应性。一旦纸张进行审查，将提供代码。
Prognostics and Health Management (PHM) is an emerging field which has received much attention from the manufacturing industry because of the benefits and efficiencies it brings to the table. And Remaining Useful Life (RUL) prediction is at the heart of any PHM system. Most recent data-driven research demand substantial volumes of labelled training data before a performant model can be trained under the supervised learning paradigm. This is where Transfer Learning (TL) and Domain Adaptation (DA) methods step in and make it possible for us to generalize a supervised model to other domains with different data distributions with no labelled data. In this paper, we propose \textit{LAMA-Net}, an encoder-decoder based model (Transformer) with an induced bottleneck, Latent Alignment using Maximum Mean Discrepancy (MMD) and manifold learning is proposed to tackle the problem of Unsupervised Homogeneous Domain Adaptation for RUL prediction. \textit{LAMA-Net} is validated using the C-MAPSS Turbofan Engine dataset by NASA and compared against other state-of-the-art techniques for DA. The results suggest that the proposed method offers a promising approach to perform domain adaptation in RUL prediction. Code will be made available once the paper comes out of review.
