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Optimum Constant-Stress Partially Accelerated Life Test Plans Using Type-I Censored Data from the Inverse Weibull Distribution

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In this paper, a constant stress partially accelerated life test (CSPALT) model is presented and analyzed using type-I censored data from the inverse Weibull distribution. The maximum likelihood estimates (point and interval) of the distribution parameters and the acceleration factor are obtained. CSPALT plans are developed. The proportion of test units that should be allocated to run under accelerated condition is optimally determined. To demonstrate the theoretical results, simulation studies are made.

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Abbreviations

PALTs:

– partially accelerated life tests

IW:

– inverse Weibull

n :

– number of test specimens in PALTs

T :

– lifetime of a specimen under normal conditions

X :

– lifetime of a specimen under accelerated conditions

η:

– censoring time of PALTs using type-I censoring

β:

– acceleration factor (β>1)

δ ui ≡ I (T i ≤ η):

ᅟ

δ aj ≡ I (X j ≤ η):

ᅟ

π:

– proportion of allocated sample specimens to run under accelerated conditions

π* :

– optimum proportion of allocated sample specimens to run under accelerated conditions

n u , n a :

– number of failed specimens under normal/accelerated conditions, respectively

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Authors and Affiliations

  1. Cairo University, Faculty of Economics & Political Science, Department of Statistics, Giza, 12613, Egypt

    Ali A. Ismail

  2. Prince Sattam Bin Abdulaziz University, College of Science and Humanities Studies, Hotat Bani Tamim, Saudi Arabia

    A. Al Tamimi

Authors
  1. Ali A. Ismail
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  2. A. Al Tamimi
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Corresponding author

Correspondence to Ali A. Ismail.

Additional information

Translated from Problemy Prochnosti, No. 6, pp. 135 – 145, November – December, 2017.

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Ismail, A.A., Al Tamimi, A. Optimum Constant-Stress Partially Accelerated Life Test Plans Using Type-I Censored Data from the Inverse Weibull Distribution. Strength Mater 49, 847–855 (2017). https://doi.org/10.1007/s11223-018-9930-8

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  • DOI: https://doi.org/10.1007/s11223-018-9930-8

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