Challenges of machine learning-based RUL prognosis: A review on NASA's C-MAPSS data set

The estimation of a system's or a component's remaining useful life (RUL) is considered the most complex task in predictive maintenance, at the same time the most beneficial one. In this brief review paper, we survey the state-of-the-art in machine learning-based RUL prognosis based on research on NASA's C-MAPSS data set. We identify the frequently used models, comparatively evaluate model performance and survey the used feature extraction methods. As a main contribution, we formulate challenges in the field, independently of the C-MAPSS data set. Among the challenges are interpretability, model uncertainty and domain adaptation, i.e. transfer learning. The identified challenges may serve to identify potential research directions, in order to further push the field of machine learning applied to RUL prognosis.