Title: Review of techniques for predicting hard drive failure with SMART attributes

Authors: Marco Garcia; Vladimir Ivanov; Anastasia Kozar; Stanislav Litvinov; Alexey Reznik; Vitaly Romanov; Giancarlo Succi

Addresses: Faculty of Computer Science and Software Engineering, Innopolis University, Innopolis 420500, Russia ' Faculty of Computer Science and Software Engineering, Innopolis University, Innopolis 420500, Russia ' Faculty of Computer Science and Software Engineering, Innopolis University, Innopolis 420500, Russia ' Faculty of Computer Science and Software Engineering, Innopolis University, Innopolis 420500, Russia ' Faculty of Computer Science and Software Engineering, Innopolis University, Innopolis 420500, Russia ' Faculty of Computer Science and Software Engineering, Innopolis University, Innopolis 420500, Russia ' Faculty of Computer Science and Software Engineering, Innopolis University, Innopolis 420500, Russia

Abstract: Hard drive failure prediction is still a relevant problem today. A number of statistical and machine learning techniques were proposed to improve failure forecasting accuracy after SMART was introduced. SMART is a diagnostics tool that aims at providing forehand failure warnings. Failure prediction methods can be viewed as a part of reliability analysis - the field that was studied intensively for decades. However, in some situations available techniques cannot be applied due to a simple reason - information at hand is not always sufficient for reliable prediction. SMART's goal is to provide meaningful information that can signify problems with the health condition of a hard drive and failure prediction techniques can leverage this data to provide timely and reliable warnings. To find the best failure forecasting algorithm and evaluate the possibility of its widespread deployment, we review existing datasets with SMART attributes, methods for feature selection for hard drive failure prediction.

Keywords: reliability; failure modelling; cyberphysical systems; machine intelligence.

DOI: 10.1504/IJMISSP.2018.092936

International Journal of Machine Intelligence and Sensory Signal Processing, 2018 Vol.2 No.2, pp.159 - 172

Received: 17 Feb 2017
Accepted: 18 Oct 2017
Published online: 03 Jul 2018 *

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