4.6 Article

An enhanced CNN-LSTM remaining useful life prediction model for aircraft engine with attention mechanism

期刊

PEERJ COMPUTER SCIENCE
卷 8, 期 -, 页码 -

出版社

PEERJ INC
DOI: 10.7717/peerj-cs.1084

关键词

Aircraft engine; Remaining useful life; Convolutional block attention module; Convolutional neural network; LSTM

资金

  1. National Natural Science Foundation of China [61873351]

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In this paper, an improved CNN-LSTM model based on CBAM is proposed for aircraft engine RUL prediction. Experimental results demonstrate the feasibility and improved accuracy and performance of our model compared to other methods.
Remaining useful life (RUL) prediction is one of the key technologies of aircraft prognosis and health management (PHM) which could provide better maintenance decisions. In order to improve the accuracy of aircraft engine RUL prediction under real flight conditions and better meet the needs of PHM system, we put forward an improved CNN-LSTM model based on the convolutional block attention module (CBAM). First, the features of aircraft engine operation data are extracted by multi -layer CNN network, and then the attention mechanism is processed by CBAM in channel and spatial dimensions to find key variables related to RUL. Finally, the hidden relationship between features and service time is learned by LSTM and the predicted RUL is output. Experiments were conducted using C-MPASS dataset. Experimental results indicate that our prediction model has feasibility. Compared with other state-of-the-art methods, the RMSE of our method decreased by 17.4%, and the score of the prediction model was improved by 25.9%.

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