关键词: 寿命预测, 多模态融合, 格拉姆角场, 卷积神经网络, 交叉注意力机制

Abstract: Aiming at the problem that engine operation data are multi-modal and difficult to achieve effective life prediction, a branching life prediction method is proposed that integrates multi-modal features of the potential relationship between images and engine time operation data. First, a sliding window was used to segment the engine operation data to construct sequence samples of engine operation data. The Gramian Angular Field (GAF) was used to convert the constructed sequence samples into images. Next, the sequence samples and images were used as a Bi-Directional Long Short-Term Memory (BiLSTM) and a Convolutional Neural Network (CNN) to obtain potential relationship features between sensors such as trends and cycles. Finally, the cross-attention mechanism (CAM)was introduced to achieve fusion of the two modal features and realize life prediction of the engine. Experimental results based on the public C-MAPSS dataset show that the R2 (R-squared) of the prediction method is higher than 0.99 and the RMSE (Root Mean Square Error) is less than 1. It can improve computational efficiency while ensuring the prediction accuracy.

Key words: life prediction, multi-modal fusion, Gramian Angular Field &#40, GAF&#41