  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.

Artificial Intelligence