Advancing aircraft engine RUL predictions: an interpretable integrated approach of feature engineering and aggregated feature importance.

ABSTRACT: In this study, we present a comprehensive approach for predicting the remaining useful life (RUL) of aircraft engines, incorporating advanced feature engineering, dimensionality reduction, feature selection techniques, and machine learning models. The process begins with a rolling time series window, followed by the extraction of a multitude of statistical features, and the application of principal component analysis for dimensionality reduction. We utilize a variety of feature selection methods, such as Genetic Algorithm, Recursive Feature Elimination, Least Absolute Shrinkage and Selection Operator Regression, and Feature Importances from a Random Forest model. As a significant contribution, we introduce the novel aggregated feature importances with cross-validation (AFICv) technique, wh

SUBMITTER: Alomari Y 

PROVIDER: S-EPMC10439180 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature