A remaining useful life prediction of turbofan engines based on SRCC and Bayes_KNN
This paper proposes a new method for predicting the remaining useful life of aircraft engines using the Spearman's rank correlation coefficient(SRCC),Bayesian,and k-nearest neighbors(KNN)algo-rithms.Firstly,to address the issue of inadequate feature extraction,the SRCC method is used to select histor-ical multidimensional monitoring features of the engine,extracting monitoring features with significant degrada-tion performance trends as inputs for the prediction model.Secondly,a KNN regression model based on Eu-clidean distance is constructed.The Bayesian optimization algorithm is utilized to train the KNN model with multiple parameters,solving the objective function and returning the optimal parameter values and minimum root mean square error of the trained model.Lastly,the probability density function of the aircraft engine's re-maining useful life(RUL)is derived to obtain the RUL prediction results.Using the CMAPSS dataset to vali-date the effectiveness of the proposed method.Experimental results demonstrate that compared to other predic-tion methods,the proposed method in this paper exhibits superior predictive performance and effectively im-proves the accuracy of aircraft engine RUL prediction.
turbofan engineSpearman's rank correlation coefficientBayesian optimization algo-rithmk-nearest neighboursremaining useful life
万方数据
维普
