Effective Prognostics and Health Management (PHM) relies on accurate prediction of the Remaining Useful Life (RUL). Data-driven RUL prediction techniques rely heavily on the representativeness of the available time-to-failure trajectories. Therefore, these methods may not perform well when applied to data from new units of a fleet that follow different operating conditions than those they were trained on. This is also known as domain shifts. Domain adaptation (DA) methods aim to address the domain shift problem by extracting domain invariant features. However, DA methods do not distinguish between the different phases of operation, such as steady states or transient phases. This can result in misalignment due to under- or over-representation of different operation phases. This paper proposes two novel DA approaches for RUL prediction based on an adversarial domain adaptation framework that considers the different phases of the operation profiles separately. The proposed methodologies align the marginal distributions of each phase of the operation profile in the source domain with its counterpart in the target domain. The effectiveness of the proposed methods is evaluated using the New Commercial Modular Aero-Propulsion System (N-CMAPSS) dataset, where sub-fleets of turbofan engines operating in one of the three different flight classes (short, medium, and long) are treated as separate domains. The experimental results show that the proposed methods improve the accuracy of RUL predictions compared to current state-of-the-art DA methods.

翻译：有效的预测与健康管理（PHM）依赖于对剩余使用寿命（RUL）的准确预测。数据驱动的RUL预测技术高度依赖于可用失效时间轨迹的代表性。因此，当这些方法应用于运行条件与训练数据不同的新机组时，其性能可能下降，这被称为域偏移。域自适应（DA）方法旨在通过提取域不变特征来解决域偏移问题。然而，DA方法无法区分运行的不同阶段，如稳态或瞬态阶段，这可能导致因不同运行阶段的欠表示或过表示而产生的对齐误差。本文提出两种基于对抗性域自适应框架的新型DA方法，该方法分别考虑运行剖面的不同阶段。所提出的方法将源域中每个运行剖面阶段的边缘分布与其在目标域中的对应阶段对齐。利用新型商用模块化航空推进系统（N-CMAPSS）数据集评估所提出方法的有效性，该数据集中运行于三种不同飞行类别（短程、中程和远程）的涡轮风扇发动机子机队被视为独立域。实验结果表明，与当前最先进的DA方法相比，所提出的方法提高了RUL预测的准确性。