  RUL estimation suffers from a server data imbalance where data from machines
near their end of life is rare. Additionally, the data produced by a machine
can only be labeled after the machine failed. Semi-Supervised Learning (SSL)
can incorporate the unlabeled data produced by machines that did not yet fail.
Previous work on SSL evaluated their approaches under unrealistic conditions
where the data near failure was still available. Even so, only moderate
improvements were made. This paper proposes a novel SSL approach based on
self-supervised pre-training. The method can outperform two competing
approaches from the literature and a supervised baseline under realistic
conditions on the NASA C-MAPSS dataset.
