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.
关键词
涡扇发动机/Spearman秩相关系数/贝叶斯优化算法/k最近邻/剩余使用寿命
Key words
turbofan engine/Spearman's rank correlation coefficient/Bayesian optimization algo-rithm/k-nearest neighbours/remaining useful life
维普
万方数据