面向时序SMART不平衡数据的硬盘故障预测算法

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Hard Disk Failure Prediction Algorithm for Time Series SMART Imbalanced Data

LI Guo'，HOU Xuexue¹，LI Jing¹，CHEN Hui² （1.School of Computer Science and Technology，Civil Aviation University of China， Tianjin 30030o，China; 2. Aviation Meteorological Center，Air Trafic Management Bureau of Civil Aviation Administration of China，Beijing 100015，China）

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 technology（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 enhance 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.

Key Words：imbalanced data；data augmentation；hard disk failure prediction；generative adversarial network；attention mechanism

0 引言

作为经济社会数字化转型的存储保障设施，云存储系统通过硬盘（harddiskdrive，HDD）来提供数据存储服务[]。（剩余13968字）