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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yu Wang Qiang Miao Ma, E.W.M. Kwok-Leung Tsui Pecht, M.G. |
| Copyright Year | 1963 |
| Abstract | 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. |
| Sponsorship | IEEE Reliability Society |
| Starting Page | 136 |
| Ending Page | 145 |
| Page Count | 10 |
| File Size | 830563 |
| File Format | |
| ISSN | 00189529 |
| Volume Number | 62 |
| Issue Number | 1 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-01-01 |
| Publisher Place | U.S.A. |
| Access Restriction | One Nation One Subscription (ONOS) |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Monitoring Redundancy Covariance matrix Hidden Markov models Feature extraction Hard disks Vectors self-monitoring, analysis, and reporting technology Hard disk drive Mahalanobis distance online anomaly detection |
| Content Type | Text |
| Resource Type | Article |
| Subject | Safety, Risk, Reliability and Quality Electrical and Electronic Engineering |
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