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Hard drive failure prediction using Decision Trees

Author

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  • Li, Jing
  • Stones, Rebecca J.
  • Wang, Gang
  • Liu, Xiaoguang
  • Li, Zhongwei
  • Xu, Ming

Abstract

This paper proposes two hard drive failure prediction models based on Decision Trees (DTs) and Gradient Boosted Regression Trees (GBRTs) which perform well in prediction performance as well as stability and interpretability. The models are evaluated on a real-world dataset containing 121,698 drives in total. Experimental results show the DT model predicts over 93% of failures at a false alarm rate under 0.01%, and the GBRT model can achieve about 90% failure detection rate without any false alarms. Moreover, the GBRT model evaluates drive health (or fault probability) which provides a quantitative indicator of failure urgency. This enables operators to allocate system resources accordingly for pre-warning migrations while maintaining the quality of user services.

Suggested Citation

  • Li, Jing & Stones, Rebecca J. & Wang, Gang & Liu, Xiaoguang & Li, Zhongwei & Xu, Ming, 2017. "Hard drive failure prediction using Decision Trees," Reliability Engineering and System Safety, Elsevier, vol. 164(C), pages 55-65.
    • Handle: RePEc:eee:reensy:v:164:y:2017:i:c:p:55-65
    DOI: 10.1016/j.ress.2017.03.004
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    References listed on IDEAS

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    1. Ye, Zhi-Sheng & Xie, Min & Tang, Loon-Ching, 2013. "Reliability evaluation of hard disk drive failures based on counting processes," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 110-118.
    2. Le Son, Khanh & Fouladirad, Mitra & Barros, Anne, 2016. "Remaining useful lifetime estimation and noisy gamma deterioration process," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 76-87.
    3. Khorasgani, Hamed & Biswas, Gautam & Sankararaman, Shankar, 2016. "Methodologies for system-level remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 8-18.
    4. Liu, Jie & Zio, Enrico, 2017. "System dynamic reliability assessment and failure prognostics," Reliability Engineering and System Safety, Elsevier, vol. 160(C), pages 21-36.
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    Cited by:

    1. Muhammad Zafran Muhammad Zaly Shah & Anazida Zainal & Taiseer Abdalla Elfadil Eisa & Hashim Albasheer & Fuad A. Ghaleb, 2023. "A Semisupervised Concept Drift Adaptation via Prototype-Based Manifold Regularization Approach with Knowledge Transfer," Mathematics, MDPI, vol. 11(2), pages 1-30, January.

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