On the Practical Interest of Discrete Inverse Pólya and Weibull-1 Models in Industrial Reliability Studies

Engineers often cope with the problem of assessing the lifetime of industrial components, on the basis of observed industrial feedback data. Usually, lifetime is modelled as a continuous random variable, for instance, exponentially or Weibull distributed. However, in some cases, the features of the...

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Published inQuality and reliability engineering international Vol. 31; no. 7; pp. 1161 - 1175
Main Authors Pasanisi, Alberto, Roero, Côme, Remy, Emmanuel, Bousquet, Nicolas
Format Journal Article
LanguageEnglish
Published Bognor Regis Blackwell Publishing Ltd 01.11.2015
Wiley Subscription Services, Inc
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ISSN0748-8017
1099-1638
DOI10.1002/qre.1845

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Abstract Engineers often cope with the problem of assessing the lifetime of industrial components, on the basis of observed industrial feedback data. Usually, lifetime is modelled as a continuous random variable, for instance, exponentially or Weibull distributed. However, in some cases, the features of the piece of equipment under investigation rather suggest the use of discrete probabilistic models. This happens for equipment that only operates on cycles or on demand. In these cases, the lifetime is rather measured in number of cycles or number of solicitations before failure; therefore, in theory, discrete models should be more appropriate. This article aims at bringing some light to the practical interest of the reliability engineer in using two discrete models among the most popular: the inverse Pólya distribution (IPD), based on a Pólya urn scheme, and the so‐called Weibull‐1 model. It is shown that for different reasons, the practical use of both models should be restricted to specific industrial situations. In particular, when nothing is a priori known over the nature of ageing and/or data are heavily right censored, they can remain of limited interest with respect to more flexible continuous lifetime models such as the usual (continuous) Weibull distribution. Nonetheless, the intuitive meaning given to the IPD could favour its use by engineers in low (decelerated) ageing situations. Copyright © 2015 John Wiley & Sons, Ltd.
AbstractList Engineers often cope with the problem of assessing the lifetime of industrial components, on the basis of observed industrial feedback data. Usually, lifetime is modelled as a continuous random variable, for instance, exponentially or Weibull distributed. However, in some cases, the features of the piece of equipment under investigation rather suggest the use of discrete probabilistic models. This happens for equipment that only operates on cycles or on demand. In these cases, the lifetime is rather measured in number of cycles or number of solicitations before failure; therefore, in theory, discrete models should be more appropriate. This article aims at bringing some light to the practical interest of the reliability engineer in using two discrete models among the most popular: the inverse Polya distribution (IPD), based on a Polya urn scheme, and the so-called Weibull-1 model. It is shown that for different reasons, the practical use of both models should be restricted to specific industrial situations. In particular, when nothing is a priori known over the nature of ageing and/or data are heavily right censored, they can remain of limited interest with respect to more flexible continuous lifetime models such as the usual (continuous) Weibull distribution. Nonetheless, the intuitive meaning given to the IPD could favour its use by engineers in low (decelerated) ageing situations.
Engineers often cope with the problem of assessing the lifetime of industrial components, on the basis of observed industrial feedback data. Usually, lifetime is modelled as a continuous random variable, for instance, exponentially or Weibull distributed. However, in some cases, the features of the piece of equipment under investigation rather suggest the use of discrete probabilistic models. This happens for equipment that only operates on cycles or on demand. In these cases, the lifetime is rather measured in number of cycles or number of solicitations before failure; therefore, in theory, discrete models should be more appropriate. This article aims at bringing some light to the practical interest of the reliability engineer in using two discrete models among the most popular: the inverse Pólya distribution (IPD), based on a Pólya urn scheme, and the so-called Weibull-1 model. It is shown that for different reasons, the practical use of both models should be restricted to specific industrial situations. In particular, when nothing is a priori known over the nature of ageing and/or data are heavily right censored, they can remain of limited interest with respect to more flexible continuous lifetime models such as the usual (continuous) Weibull distribution. Nonetheless, the intuitive meaning given to the IPD could favour its use by engineers in low (decelerated) ageing situations. Copyright © 2015 John Wiley & Sons, Ltd.
Author Bousquet, Nicolas
Pasanisi, Alberto
Remy, Emmanuel
Roero, Côme
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  organization: Industrial Risk Management Department, EDF R&D, 6 quai Watier, 78401, Chatou, France
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– reference: Ivanova A, Rosenberger WF, Durham SD, Flournoy N. A birth and death urn for randomized clinical trials: asymptotic methods. Sankhya: The Indian Journal of Statistics, Series B. 2000; 62:104-118.
– reference: Gupta PL, Gupta RC, Tripathi RC. On the monotonic properties of the discrete failure rates. Journal of Statistical Planning and Inference. 1997; 65(2):225-268.
– reference: Almalki SJ, Nadarajah S. A new discrete modified Weibull distribution. IEEE Transactions on Reliability. 2014; 63(1):68-80.
– reference: Sheu SH. A generalized age and block replacement of a system subject to shocks. European Journal of Operational Research. 1998; 108(2):345-362.
– reference: Mahmoud H. Polya Urn Models, Chapman & Hall/CRC Texts in Statistical Science. Taylor & Francis: Boca Raton FL, 2008.
– reference: Castino F, Festa R, Ratto CF. Stochastic modelling of wind velocities time series. Journal of Wind Engineering and Industrial Aerodynamics. 1998; 74-76:141-151.
– reference: Reich NG, Lessler J, Cummings DAT, Brookmeyer R. Estimating absolute and relative case fatality ratios from infectious disease surveillance data. Biometrics. 2012; 68(2):598-606.
– reference: Shaked M, Shanthikumar JG, Valdez-Torres JB. Discrete hazard rate functions. Computers & Operations Research. 1995; 22(4):391-402.
– reference: Wang CH, Sheu SH. Optimal lot sizing for products sold under free-repair warranty. European Journal of Operational Research. 2003; 149(1):131-141.
– reference: Nakagawa T, Osaki S. The discrete Weibull distribution. IEEE Transactions on Reliability. 1975; 5(24):300-301.
– reference: Wang WY, Sheu SH, Chen YC, Horng DJ. Economic optimization of off-line inspection with rework consideration. European Journal of Operational Research. 2009; 194(3):807-813.
– reference: Stein WE, Dattero R. A new discrete Weibull distribution. IEEE Transactions on Reliability. 1984; 33(2):196-197.
– reference: Grassi M, Colombo I, Lapasin R. Drug release from an ensemble of swellable crosslinked polymer particles. Journal of Controlled Release. 2000; 68(1):97-113.
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Snippet Engineers often cope with the problem of assessing the lifetime of industrial components, on the basis of observed industrial feedback data. Usually, lifetime...
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SubjectTerms ageing
Aging
censored data
Deceleration
discrete survival data
discrete Weibull model
Inverse
inverse Pólya model
Probabilistic methods
Probability theory
Random variables
Reliability analysis
Weibull distribution
Title On the Practical Interest of Discrete Inverse Pólya and Weibull-1 Models in Industrial Reliability Studies
URI https://api.istex.fr/ark:/67375/WNG-28CTSLHW-J/fulltext.pdf
https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fqre.1845
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https://www.proquest.com/docview/1770300999
Volume 31
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