  This work presents a novel semi-supervised learning approach for data-driven
modeling of asset failures when health status is only partially known in
historical data. We combine a generative model parameterized by deep neural
networks with non-linear embedding technique. It allows us to build prognostic
models with the limited amount of health status information for the precise
prediction of future asset reliability. The proposed method is evaluated on a
publicly available dataset for remaining useful life (RUL) estimation, which
shows significant improvement even when a fraction of the data with known
health status is as sparse as 1% of the total. Our study suggests that the
non-linear embedding based on a deep generative model can efficiently
regularize a complex model with deep architectures while achieving high
prediction accuracy that is far less sensitive to the availability of health
status information.
