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 wide- spread application to other prediction situations with data from numerous sensors.

Automated summary

This paper proposes a novel approach that combines knowledge and deep learning models for the remaining useful life (RUL) prediction. By representing the sensor relationships as flow charts and transforming them into embedding vectors for clustering, the proposed approach guides the arrangement of sensor data and the construction of hybrid deep learning models. The robustness and reliability of the approach are demonstrated on the NASA open dataset C-MAPSS, showing improved prediction accuracy compared to existing methods. The interpretable deep learning model constructed using knowledge highlights the feasibility and reliability of fusing knowledge and deep learning models.