Probabilistic prediction of aero engine remaining useful life based on Bayesian graph attention Transformer

As the heart of the aircraft, the health of the aircraft engine is very important to the safe flight of the aircraft. The powerful data mining capability of deep learning provides a new method for predicting the remaining useful life of aero engine through massive historical data. However, most of the traditional methods based on deep learning focus on mining the correlation of data in time, and ignore the complex non-Euclidian spatial relationship between the monitoring data of multiple sensors. In addition, few studies consider the uncertainty of the data or the prediction process itself, and the reliability of the prediction results is lacking. To solve the above problems, a probabilistic prediction method of aero engine remaining service life based on Bayesian network and graph attention Transformer is proposed in this paper. The graph attention mechanism is integrated into Transformer's time-multi-attention module, and combined with the advantages of graph attention network in spatial feature extraction and Transformer model in temporal feature extraction, the joint extraction of spatial-temporal relationship of data features can be realized. At the same time, the improved Bayesian network is used to measure the prediction uncertainty, and the corresponding confidence interval is given when the remaining service life prediction point is obtained. Finally, experiments on open aero-engine data sets prove the effectiveness and advancement of the proposed model.