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.

翻译：时间失效（TTF）轨迹的代表性数据有限，要么限制了基于深度学习（DL）的剩余寿命（RUL）预测方法在实际中的应用性能，甚至阻碍了其应用。生成具有物理合理性的合成数据是应对这一挑战的有效途径。本研究提出了一种新颖的混合框架，将受控的物理信息数据生成方法与基于深度学习的预测模型相结合用于预测性维护。在该框架中，开发了一种新的受控物理信息生成对抗网络（CPI-GAN），用于生成具有物理可解释性和多样性的合成退化轨迹。提出了五种基本物理约束作为生成器中的可控设置。设计了一个带惩罚项的物理信息损失函数作为正则化项，确保合成数据中记录的系统健康状态变化趋势与潜在物理规律一致。随后，生成的合成数据作为基于深度学习的预测模型的输入，以获得RUL估计。该框架基于新型商用模块化航空推进系统仿真（N-CMAPSS）数据集（一种涡扇发动机预测数据集，假设TTF轨迹可用性有限）进行评估。实验结果表明，所提框架能够生成与潜在退化趋势一致的合成TTF轨迹，并显著提升RUL预测的准确性。