  Limited availability of representative time-to-failure (TTF) trajectories
either limits the performance of deep learning (DL)-based approaches on
remaining useful life (RUL) prediction in practice or even precludes their
application. Generating synthetic data that is physically plausible is a
promising way to tackle this challenge. In this study, a novel hybrid framework
combining the controlled physics-informed data generation approach with a deep
learning-based prediction model for prognostics is proposed. In the proposed
framework, a new controlled physics-informed generative adversarial network
(CPI-GAN) is developed to generate synthetic degradation trajectories that are
physically interpretable and diverse. Five basic physics constraints are
proposed as the controllable settings in the generator. A physics-informed loss
function with penalty is designed as the regularization term, which ensures
that the changing trend of system health state recorded in the synthetic data
is consistent with the underlying physical laws. Then, the generated synthetic
data is used as input of the DL-based prediction model to obtain the RUL
estimations. The proposed framework is evaluated based on new Commercial
Modular Aero-Propulsion System Simulation (N-CMAPSS), a turbofan engine
prognostics dataset where a limited avail-ability of TTF trajectories is
assumed. The experimental results demonstrate that the proposed framework is
able to generate synthetic TTF trajectories that are consistent with underlying
degradation trends. The generated trajectories enable to significantly improve
the accuracy of RUL predictions.
