A Remaining Useful Life Prediction of Aero Engines Based on MIC Feature Extraction and BO-CatBoost
Aimed at the problems that the degradation parameters monitored by aviation engine sensors are difficult to be extracted,is subject to noise interference,and accuracy is insufficient in predicting engine remaining useful Life(RUL),a new remaining useful life prediction model is proposed by utilizing the maximal information coefficient(MIC),bayesian optimization(BO)algorithm,and categorical boosting(CatBoost)algorithm.Firstly,to effectively address the issue of inadequate feature extraction,the col-lected historical monitoring features of sensors are subjected to maximal information coefficient correlation calculation to extract key degradation features from the significant impact on the engine's operational lifes-pan.Secondly,to address the gradient bias and prediction offset issues in remaining useful life prediction,the categorical boosting algorithm method based on bayesian optimization is employed to predict the remai-ning useful life of aero engines.Finally,experiments are conducted on the commercial modular aero-pro-pulsion system simulation(C-MAPSS)dataset provided by national aeronautics and space administration(NASA).The results show that the proposed prediction method has a good performance,and is valid.
万方数据
维普
