To Fail or Not to Fail: Predicting Hard Disk Drive Failure Time Windows

  • Marwin ZüfleEmail author
  • Christian Krupitzer
  • Florian Erhard
  • Johannes Grohmann
  • Samuel Kounev
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12040)


Due to the increasing size of today’s data centers as well as the expectation of 24/7 availability, the complexity in the administration of hardware continuously increases. Techniques as the Self-Monitoring, Analysis, and Reporting Technology (S.M.A.R.T.) support the monitoring of the hardware. However, those techniques often lack algorithms for intelligent data analytics. Especially, the integration of machine learning to identify potential failures in advance seems to be promising to reduce administration overhead. In this work, we present three machine learning approaches to (i) identify imminent failures, (ii) predict time windows for failures, as well as (iii) predict the exact time-to-failure. In a case study with real data from 369 hard disks, we achieve an F1-score of up to 98.0% and 97.6% for predicting potential failures with two or multiple time windows, respectively, and a hit rate of 84.9% (with a mean absolute error of 4.5 h) for predicting the time-to-failure.


Failure prediction S.M.A.R.T. Machine learning Labeling methods Classification Regression Cloud Computing 



This work was co-funded by the German Research Foundation (DFG) under grant No. (KO 3445/11-1) and the IHK (Industrie- und Handelskammer) Würz-burg-Schweinfurt.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Marwin Züfle
    • 1
    Email author
  • Christian Krupitzer
    • 1
  • Florian Erhard
    • 1
  • Johannes Grohmann
    • 1
  • Samuel Kounev
    • 1
  1. 1.Software Engineering GroupUniversity of WürzburgWürzburgGermany

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