  In this paper, a data-driven diagnostic and prognostic approach based on
machine learning is proposed to detect laser failure modes and to predict the
remaining useful life (RUL) of a laser during its operation. We present an
architecture of the proposed cognitive predictive maintenance framework and
demonstrate its effectiveness using synthetic data.
