Feature Selection for Remaining Useful Life Prediction in Hard Disk Drives with Missing Data - Felix, Gabriel L. S.; Pereira, Francisco L. F.; Praciano, Francisco D. B. S.; Gomes, João P. P.; Machado, Javam C. (Anais Estendidos do Simpósio Brasileiro de Banco de Dados (SBBD), 2023)
This paper proposes a two-stage feature selection approach for the problem of Remaining Useful Life (RUL) prediction in Hard Disk Drives (HDDs) with missing data. First, a wrapper method is employed, utilizing a regression estimator to identify the most informative features for RUL prediction. The selected feature set is then evaluated in the second stage using a neural network model, with a focus on assessing the imputation performance for missing data. The goal is to determine a feature subset that enhances RUL prediction accuracy and exhibits robustness in handling missing data scenarios. This approach addresses the challenges of missing data and provides insights into the most relevant features for accurate RUL prediction.