  In Prognostics and Health Management (PHM) sufficient prior observed
degradation data is usually critical for Remaining Useful Lifetime (RUL)
prediction. Most previous data-driven prediction methods assume that training
(source) and testing (target) condition monitoring data have similar
distributions. However, due to different operating conditions, fault modes,
noise and equipment updates distribution shift exists across different data
domains. This shift reduces the performance of predictive models previously
built to specific conditions when no observed run-to-failure data is available
for retraining. To address this issue, this paper proposes a new data-driven
approach for domain adaptation in prognostics using Long Short-Term Neural
Networks (LSTM). We use a time window approach to extract temporal information
from time-series data in a source domain with observed RUL values and a target
domain containing only sensor information. We propose a Domain Adversarial
Neural Network (DANN) approach to learn domain-invariant features that can be
used to predict the RUL in the target domain. The experimental results show
that the proposed method can provide more reliable RUL predictions under
datasets with different operating conditions and fault modes. These results
suggest that the proposed method offers a promising approach to performing
domain adaptation in practical PHM applications.
