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Reliability evaluation of hard disk drive failures based on counting processes

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  • Ye, Zhi-Sheng
  • Xie, Min
  • Tang, Loon-Ching

Abstract

Reliability assessment for hard disk drives (HDDs) is important yet difficult for manufacturers. Motivated by the fact that the particle accumulation in the HDDs, which accounts for most HDD catastrophic failures, is contributed from the internal and external sources, a counting process with two arrival sources is proposed to model the particle cumulative process in HDDs. This model successfully explains the collapse of traditional ALT approaches for accelerated life test data. Parameter estimation and hypothesis tests for the model are developed and illustrated with real data from a HDD test. A simulation study is conducted to examine the accuracy of large sample normal approximations that are used to test existence of the internal and external sources.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:109:y:2013:i:c:p:110-118
    DOI: 10.1016/j.ress.2012.07.003
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    References listed on IDEAS

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    1. Ye, Zhi-Sheng & Shen, Yan & Xie, Min, 2012. "Degradation-based burn-in with preventive maintenance," European Journal of Operational Research, Elsevier, vol. 221(2), pages 360-367.
    2. Mohammadian, S. Hossein & Aït-Kadi, Daoud, 2010. "Design stage confirmation of lifetime improvement for newly modified products through accelerated life testing," Reliability Engineering and System Safety, Elsevier, vol. 95(8), pages 897-905.
    3. Loon Tang & Shao Lam & Quock Ng & Jing Goh, 2010. "A reliability modeling framework for the hard disk drive development process," IISE Transactions, Taylor & Francis Journals, vol. 42(4), pages 260-272.
    4. Taghipour, Sharareh & Banjevic, Dragan, 2011. "Trend analysis of the power law process using Expectation–Maximization algorithm for data censored by inspection intervals," Reliability Engineering and System Safety, Elsevier, vol. 96(10), pages 1340-1348.
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    Cited by:

    1. Arslan, Suayb S. & Peng, James & Goker, Turguy, 2020. "A data-assisted reliability model for carrier-assisted cold data storage systems," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    2. Van Dyck, Jozef & Verdonck, Tim, 2014. "Precision of power-law NHPP estimates for multiple systems with known failure rate scaling," Reliability Engineering and System Safety, Elsevier, vol. 126(C), pages 143-152.
    3. 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.
    4. Yang, Li & Ma, Xiaobing & Peng, Rui & Zhai, Qingqing & Zhao, Yu, 2017. "A preventive maintenance policy based on dependent two-stage deterioration and external shocks," Reliability Engineering and System Safety, Elsevier, vol. 160(C), pages 201-211.
    5. Wu, Shaomin, 2021. "Two methods to approximate the superposition of imperfect failure processes," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    6. Zhu, Xiaoyan & Wang, Jun & Yuan, Tao, 2019. "Design and maintenance for the data storage system considering system rebuilding process," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    7. Slimacek, Vaclav & Lindqvist, Bo Henry, 2016. "Nonhomogeneous Poisson process with nonparametric frailty," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 14-23.

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