基于混合高斯分布的硬盘故障检测

Lucas P. Queiroz, Francisco Caio M. Rodrigues, J. Gomes, Felipe T. Brito, Iago C. Brito, Javam C. Machado
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引用次数: 6

摘要

能够检测硬盘驱动器(HDD)中的故障可以为计算机制造商、用户和存储系统提供商带来重大利益。因此,一些工作集中在hdd故障检测算法的开发上。近年来,利用SMART(自我监测分析和报告技术)特征和基于马氏距离的异常检测算法的方法取得了可喜的结果。然而,当数据的正态性假设不成立时,这些方法的性能可能会严重下降。为了克服这一问题,我们提出了一种基于高斯混合模型(GMM)的硬盘故障检测新方法。在实际数据集中对该方法进行了测试,并将其性能与其他三种HDD故障检测方法进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fault Detection in Hard Disk Drives Based on Mixture of Gaussians
Being able to detect faults in Hard Disk Drives (HDD) can lead to significant benefits to computer manufacturers, users and storage system providers. As a consequence, several works have focused on the development of fault detection algorithms for HDDs. Recently, promising results were achieved by methods using SMART (Self-Monitoring Analysis and Reporting Technology) features and anomaly detection algorithms based on Mahalanobis distance. Nevertheless, the performance of such methods can be seriously degraded when the normality assumption of the data does not hold. As a way to overcome this issue, we propose a new method for fault detection in HDD based on a Gaussian Mixture Model (GMM). The proposed method is tested in a real world dataset and its performance is compared to three other HDD fault detection methods.
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