Remaining Life Prediction of Turbofan Engine Based on Feature Selection and Transformer
Aiming at the problem that the traditional residual service life prediction model is difficult to solve the problem of long-term dependence and that different feature combinations have a great impact on the prediction accuracy of the model,a residual service life prediction model based on feature selection and Transformer was proposed.The maximum correlation and minimum redundancy fea-ture selection algorithm based on mutual information was used to capture the relationship between features and labels,features and fea-tures,and the best feature combination was obtained.Then taking Transformer's encoder as the main body and adding the gated convolu-tion unit,a prediction model was formed,so that the model could fully capture the global information and improve the operational effi-ciency,and pay more attention to local information.The model parameters were determined by particle swarm optimization.Finally,the variable data of the optimal feature combination were input into the model to realize the prediction of the remaining service life of the turbofan engine.This method was verified in C-MAPSS data set,and comparative experiments were carried out.The results show that the prediction error and model efficiency are improved to some extent.
remaining service lifemaximum correlation and minimum redundancyfeature selectionmutual informationTrans-former model
万方数据
维普
