Online Anomaly Detection for Hard Disk Drives Based on Mahalanobis Distance

A hard disk drive (HDD) failure may cause serious data loss and catastrophic consequences. Online health monitoring provides information about the degradation trend of the HDD, and hence the early warning of failures, which gives us a chance to save the data. This paper developed an approach for HDD...

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Bibliographic Details
Published in:IEEE transactions on reliability 2013-03, Vol.62 (1), p.136-145
Main Authors: Yu Wang, Qiang Miao, Ma, E. W. M., Kwok-Leung Tsui, Pecht, M. G.
Format: Article
Language:English
Subjects:
analysis
and reporting technology
Anomalies
Backups
Covariance matrix
Disk drives
Failure
Failure modes
False alarms
Feature extraction
Hard disk drive
Hard disks
Hidden Markov models
Mahalanobis distance
Monitoring
On-line systems
Online
online anomaly detection
Redundancy
self-monitoring
Studies
Vectors
Warning
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Summary:A hard disk drive (HDD) failure may cause serious data loss and catastrophic consequences. Online health monitoring provides information about the degradation trend of the HDD, and hence the early warning of failures, which gives us a chance to save the data. This paper developed an approach for HDD anomaly detection using Mahalanobis distance (MD). Critical parameters were selected using failure modes, mechanisms, and effects analysis (FMMEA), and the minimum redundancy maximum relevance (mRMR) method. A self-monitoring, analysis, and reporting technology (SMART) data set is used to evaluate the performance of the developed approach. The result shows that about 67% of the anomalies of failed drives can be detected with zero false alarm rate, and most of them can provide users with at least 20 hours during which to backup the data.
ISSN:0018-9529
1558-1721
DOI:10.1109/TR.2013.2241204