Feature Selection for Remaining Useful Life Prediction in Hard Disk Drives with Missing Data

• Gabriel L. S. Felix Universidade Federal do Ceará (UFC)
• Francisco L. F. Pereira Universidade Federal do Ceará (UFC)
• Francisco D. B. S. Praciano Universidade Federal do Ceará (UFC)
• João P. P. Gomes Universidade Federal do Ceará (UFC)
• Javam C. Machado Universidade Federal do Ceará (UFC)

DOI: https://doi.org/10.5753/sbbd_estendido.2023.233372

Resumo

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.

Palavras-chave: HDD, RUL, Failure prediction, Deep Learning, Feature Selection

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