Proceedings of the

The 33rd European Safety and Reliability Conference (ESREL 2023)
3 – 8 September 2023, Southampton, UK

Generating Controlled Physics-Informed Time-to-failure Trajectories for Prognostics in Unseen Operational Conditions

Jiawei Xiong1, Jian Zhou1, Yizhong Ma1,a and Olga Fink2

1School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China.

2Intelligent Maintenance and Operations Systems, EPFL, Station 18, Lausanne, 1015, Switzerland.


The performance of deep learning (DL)-based methods for predicting remaining useful life (RUL) may be limited in practice due to the scarcity of representative time-to-failure (TTF) data. To overcome this challenge, generating physically plausible synthetic data is a promising approach. In this study, a novel hybrid framework is proposed that combines a controlled physics-informed data generation approach with a DL-based prediction model for prognostics. The framework introduces a new controlled physics-informed generative adversarial network (CPI-GAN) that gen-erates diverse and physically interpretable synthetic degradation trajectories. The generator includes five basic phys-ics constraints that serve as controllable settings. The regularization term, which is a physics-informed loss function with a penalty, ensures that the synthetic data's changing health state trend complies with the underlying physical laws. The synthetic data is then fed to the DL-based prediction model to estimate RUL. The framework's effective-ness is evaluated using the New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS), a turbo-fan engine prognostics dataset with limited TTF trajectories. The experimental results demonstrate that the proposed framework can generate synthetic TTF trajectories that are consistent with underlying degradation trends and sig-nificantly improve RUL prediction accuracy.

Keywords: Prognostics, Time-to-failure trajectory generation, Deep learning, Physics-informed generative adver-sarial networks.

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