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General Information
    • ISSN: 2010-3689
    • Frequency: Bimonthly (2011-2014); Monthly (Since 2015)
    • DOI: 10.18178/IJIET
    • Editor-in-Chief: Prof. Dr. Steve Thatcher
    • Executive Editor: Ms. Nancy Y. Liu
    • Abstracting/ Indexing: EI (INSPEC, IET), Electronic Journals Library, Google Scholar, Crossref and ProQuest
    • E-mail: ijiet@ejournal.net
Editor-in-chief
Prof. Dr. Steve Thatcher
QUniversity, Australia
It is my honor to be the editor-in-chief of IJIET. The journal publishes good-quality papers which focous on the advanced researches in the field of information and education technology. Hopefully, IJIET will become a recognized journal among the scholars in the related fields.

IJIET 2018 Vol.8(7): 471-481 ISSN: 2010-3689
doi: 10.18178/ijiet.2018.8.7.1085

Big Data Preventive Maintenance for Hard Disk Failure Detection

Chuan-Jun Su and Jorge A. Quan Yon
Abstract—In recent years, with the rapid advancement of cloud technology, data centers have always been considered one of cloud services most important aspect to evaluate, and its reliability and availability have been the focus of every IT engineer’s attention. However, service interruption is the most important factor to consider for every data center, affecting the user experience, or causing loss in a business. Therefore, automated fault prevention and monitoring of data center will effectively improve the reliability of cloud services. Predictive maintenance differs from traditional maintenance process (i.e. routine maintenance and corrective maintenance), it evaluates the state by performing device condition monitoring, and according to the state, it predicts when maintenance should be performed.
This research focuses on hard drive failure prediction, with big data analysis and machine learning technology, we have developed a Preventive Monitoring System (PMS). Utilizing Prognostics and Health Management (PHM) to identify the failure mechanism, and combining Self-Monitoring, Analysis and Reporting Technology (SMART) to identify early signs of abnormalities before the device fail. Finally, we use random forest algorithm to construct the predictive model. This research aims to develop a predictive monitoring system to provide device condition monitoring and fault diagnosis, thereby identifying the device malfunction and resolving it as soon as possible, keeping the system maintained at optimal condition.

Index Terms—Big data, preventive maintenance, failure prediction, hard drive, random forest.

The authors are with the Department of Industrial Engineering and Management, Yuan Ze University, Taiwan (e-mail: iecjsu@saturn.yzu.edu.tw, s1038908@saturn.yzu.edu.tw).

[PDF]

Cite: Chuan-Jun Su and Jorge A. Quan Yon, "Big Data Preventive Maintenance for Hard Disk Failure Detection," International Journal of Information and Education Technology vol. 8, no. 7, pp. 471-481, 2018.

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