Remaining useful life prediction of aero-engine enabled by fusing knowledge and deep learning models

The remaining useful life (RUL) prediction of a complex engineering system is extremely significant for ensuring system reliability. The conventional prediction of the RUL based on only extracted degradation features of sensor data is tedious for decreasing costs and providing a decision-making foundation. However, knowledge is available for improving RUL prediction accuracy. This paper proposes a novel RUL prediction approach that combines knowledge and deep learning models. The proposed approach represents the sensor relationships as flow charts to be transformed as embedding vectors for clustering. These clustering results are subsequently utilized to guide the sensor data arrangement and hybrid deep learning model construction. Compared to various deep learning models, the robustness and reliability of the proposed method are demonstrated on the NASA open dataset C-MAPSS. The results show that the proposed approach had improved prediction accuracy by 5.5% compared to the best prediction from the literature methods. Furthermore, the constructed deep learning model by utilizing knowledge can be interpretable. Most importantly, the prediction results reveal the feasibility and reliability of fusing knowledge and deep learning models. And the proposed approach is promising for widespread application to other prediction situations with data from numerous sensors.