Predicting Failures in Hard Drives with LSTM Networks

被引:33
作者
Lima, Fernando Dione S. [1 ]
Amaral, Gabriel M. R. [1 ]
Leite, Lucas G. M. [1 ]
Gomes, Joao Paulo P. [1 ]
Machado, Javam C. [1 ]
机构
[1] Univ Fed Ceara, Dept Sci Comp, LSBD, Fortaleza, Ceara, Brazil
来源
2017 6TH BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS) | 2017年
关键词
D O I
10.1109/BRACIS.2017.72
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Several research has been done to propose early failure detection techniques for hard disk drives in order to improve storage systems availability and avoid data loss. Failure prediction in such circumstances would allow for the reduction of downtime costs through anticipated disk replacements. Many of the techniques proposed so far mainly perform incipient failure detection thus not allowing for proper planning of such maintenance tasks. Others perform well only under a limited prediction horizon. In this work, we present a remaining useful life estimation approach for hard disk drives based on SMART parameters that is capable of predicting failures in both long and short term intervals by leveraging the capabilities of LSTM networks.
引用
收藏
页码:222 / 227
页数:6
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