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Economic allocation of reliability growth testing using Weibull distributions

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  • Awad, Mahmoud

Abstract

Reliability growth testing (RGT) has been widely used for assessing the reliability of complex systems in many industries such as automotive, aerospace, and oil and gas industry. The traditional common and practiced approach of RGT is to assess the initial reliability of the system by building and testing few prototypes for a period of time that extends from few months to years. Then, based on the initial reliability, initial testing time, and reliability target; the total testing time is determined using power law based models such as Duane and AMSAA/Crow models. In this paper, a new method is proposed to allocate RGT time for both subsystems and system level in order to minimize system failure intensity under limited cost and time resources. Unlike existing methods, failure intensity is assumed to be dynamic and modeled using Weibull distribution. Modeling using Weibull is more realistic and increases the applicability of the proposed method in real life applications. The proposed method is motivated by real life examples and its effectiveness is demonstrated by real-life examples.

Suggested Citation

  • Awad, Mahmoud, 2016. "Economic allocation of reliability growth testing using Weibull distributions," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 273-280.
    • Handle: RePEc:eee:reensy:v:152:y:2016:i:c:p:273-280
    DOI: 10.1016/j.ress.2016.03.012
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Heydari, Mohammadhossein & Sullivan, Kelly M., 2019. "Robust allocation of testing resources in reliability growth," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
    2. Sonal, S.D. & Ammanagi, S & Kanjilal, O & Manohar, C.S., 2018. "Experimental estimation of time variant system reliability of vibrating structures based on subset simulation with Markov chain splitting," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 55-68.
    3. Peng, Yizhen & Wang, Yu & Zi, YanYang & Tsui, Kwok-Leung & Zhang, Chuhua, 2017. "Dynamic reliability assessment and prediction for repairable systems with interval-censored data," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 301-309.
    4. Wang, Yukun & Liu, Yiliu & Li, Xiaopeng & Chen, Junyan, 2019. "Multi-phase reliability growth test planning for repairable products sold with a two-dimensional warranty," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 315-326.
    5. Yihai He & Changchao Gu & Zhaoxiang Chen & Xiao Han, 2017. "Integrated predictive maintenance strategy for manufacturing systems by combining quality control and mission reliability analysis," International Journal of Production Research, Taylor & Francis Journals, vol. 55(19), pages 5841-5862, October.
    6. Wei Wang & Yaofeng Xu & Liguo Hou, 2019. "Optimal allocation of test times for reliability growth testing with interval-valued model parameters," Journal of Risk and Reliability, , vol. 233(5), pages 791-802, October.

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