A Data-driven Digital Model for Remaining Useful Life Prediction of Aero-engines

In this work, a data-driven framework based on digital model is proposed to predict the remaining useful life (RUL) of aero-engines. An encoder-decoder structure is applied to generate a hidden representation of the degradation status of aero-engines. The digital model achieves high similarity between real sensor data and the virtual sensor data generated by the digital model. A hidden representation with lower dimension is generated by data-driven model, which contains the vital features of the aero-engines. A RUL prediction block is also proposed to map the hidden representation into the final RUL result. The NASA C-MAPSS dataset is used to evaluate the performance of the proposed method. The loss between the virtual sensor data from the digital model and the actual sensor data indicates that our proposed data-driven model well represents the physical engine model. Evaluation results of RMSE and Score show that the framework achieves accurate prediction of the remaining useful life. In addition, the proposed framework improves the interpret-ability in the process of data processing and feature extraction, and has high applicability in predictive maintenance.