Remaining Useful Life Estimation of Aircraft Engines Based on Deep Convolution Neural Network and LightGBM Combination Model

Accurately predicting the remaining useful life (RUL) of aero-engines is of great significance for improving the reliability and safety of aero-engine systems. Because of the high dimension and complex features of sensor data in RUL prediction, this paper proposes a model combining deep convolution neural networks (DCNN) and the light gradient boosting machine (LightGBM) algorithm to estimate the RUL. Compared with traditional prognostics and health management (PHM) techniques, signal processing of raw sensor data and prior expertise are not required. The procedure is shown as follows. First, the time window of raw data of the aero-engine is used as the input of DCNN after normalization. The role of DCNN is to extract information from the input data. Second, considering the limitations of the fully connected layer of DCNN, we replace it with a strong classifier-LightGBM to improve the accuracy of prediction. Finally, to prove the effectiveness of the proposed method, we conducted some experiments on the C-MAPSS data set provided by NASA, and obtained good accuracy. By comparing the prediction effect with other commonly used algorithms on the same data set, the proposed algorithm has obvious advantages.

Automated summary

This study proposes a model combining DCNN and LightGBM algorithm to estimate the RUL of aero-engines, without the need for signal processing or prior expertise on raw sensor data. Experimental results show good accuracy on the NASA data set, proving the effectiveness of this method.