郑州大学学报（理学版）2025，Vol.57Issue(3)：19-27,9.DOI:10.13705/j.issn.1671-6841.2024006
面向时序SMART不平衡数据的硬盘故障预测算法
Hard Disk Failure Prediction Algorithm for Time Series SMART Imbalanced Data
摘要
Abstract
In response to the issue of poor fault prediction caused by the scarcity of data center hard disk failure data,a hard disk failure prediction algorithm that could solve imbalance problems through data augmentation was proposed based on the temporal features of self-monitoring analysis and reporting tech-nology(SMART)data information.The algorithm employed long short-term memory networks to improve traditional generative adversarial networks,and sequence segment data containing fault deterioration trend information was generated to address the imbalance problem in the dataset.Meanwhile,to further en-hance predictive performance,the prediction model was integrated with temporal attention mechanism and feature attention mechanism,exploring the sensitivity of different SMART features and time steps to the deterioration process of hard disk failures.Additionally,various typical feature selection methods were combined in the feature selection stage to select key features.Experimental validation was conducted on a real hard disk dataset,and the results indicated that the accuracy,recall and F1 values of the proposed algorithm were significantly improved.关键词
不平衡数据/数据增强/硬盘故障预测/生成对抗网络/注意力机制Key words
imbalanced data/data augmentation/hard disk failure prediction/generative adversarial network/attention mechanism分类
信息技术与安全科学引用本文复制引用
李国,侯雪雪,李静,陈辉..面向时序SMART不平衡数据的硬盘故障预测算法[J].郑州大学学报（理学版）,2025,57(3):19-27,9.基金项目
国家自然科学基金项目(U2233214) （U2233214）
天津市教委科研资助项目(2021KJ044) （2021KJ044）
