A Novel Evaluation Framework for Unsupervised Domain Adaption on Remaining Useful Lifetime Estimation

Unsupervised Domain Adaption (DA) is an approach for adapting a data-driven model to new data without labels. Recent work on Remaining Useful Lifetime (RUL) estimation of aero engines yielded promising results for this approach. However, the current evaluation framework for DA is of limited significance when used for RUL estimation. It assumes a use case where a large number of fully degraded systems are available for adaption, which makes unsupervised DA in itself unnecessary. It is shown that the current framework overestimates adaption performance and obscures potential, negative effects of DA on performance. We propose a novel evaluation framework for unsupervised DA, specialized in RUL estimation, that takes the number of available systems and their grade of degradation into account. It enables an informed performance comparison of DA methods. We detail the framework's capabilities on two DA methods and show that unsupervised DA delivers improved RUL estimations under real-life scenarios, as well.