GRUHP : An Adaptive Feature Selection Model for Hard Disk Drive Failure Prediction in Large‐Scale Storage Systems

Hard disk drive (HDD) failures in large‐scale distributed storage systems can lead to severe data loss and service disruptions. While failure prediction using SMART data is a critical mitigation strategy, existing models often inadequately capture the gradual degradation of disk health, suffering from limitations in feature selection and temporal modeling. To overcome these challenges, this paper proposes GRUHP, a Gated Recurrent Unit‐based Health Prediction model integrated with an adaptive feature selection mechanism. GRUHP efficiently handles high‐dimensional Self‐Monitoring, Analysis, and Reporting Technology (SMART) data by dynamically identifying the most discriminative features, while its GRU architecture fully leverages temporal patterns for accurate health assessment. The model also incorporates dedicated modules for continuous health state evaluation and fault diagnosis. Extensive experiments on two public datasets demonstrate that GRUHP achieves an average precision of 93.4%, recall of 99.3%, and F 0.5 ‐score of 94.5%, with a false positive rate of only 0.7%. These results confirm that the proposed method, through its synergistic feature selection and temporal modeling, offers a robust and highly applicable solution for proactive failure prediction in real‐world storage environments.