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 in | Quality and reliability engineering international Vol. 31; no. 7; pp. 1161 - 1175 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Bognor Regis
Blackwell Publishing Ltd
01.11.2015
Wiley Subscription Services, Inc |
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Online Access | Get full text |
ISSN | 0748-8017 1099-1638 |
DOI | 10.1002/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. |
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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 |
Author_xml | – sequence: 1 givenname: Alberto surname: Pasanisi fullname: Pasanisi, Alberto email: Correspondence to: Alberto Pasanisi, European Institute for Energy Research, Emmy-Noether-Str. 11, 76131 Karlsruhe, Germany., alberto.pasanisi@eifer.org organization: European Institute for Energy Research, Emmy-Noether-Str. 11, 76131, Karlsruhe, Germany – sequence: 2 givenname: Côme surname: Roero fullname: Roero, Côme organization: Mathematics Department, INRIA - University Paris-Sud, Bat. 425, 91405, Orsay, France – sequence: 3 givenname: Emmanuel surname: Remy fullname: Remy, Emmanuel organization: Industrial Risk Management Department, EDF R&D, 6 quai Watier, 78401, Chatou, France – sequence: 4 givenname: Nicolas surname: Bousquet fullname: Bousquet, Nicolas organization: Industrial Risk Management Department, EDF R&D, 6 quai Watier, 78401, Chatou, France |
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References_xml | – reference: Finkelstein M. Failure Rate Modelling for Reliability and Risk. Springer: London, 2008. – 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. – reference: Rinne H. The Weibull Distribution: A Handbook. Taylor & Francis: Boca Raton FL, 2008. – reference: Almalki SJ, Nadarajah S. Modifications of the Weibull distribution: a review. Reliability Engineering and System Safety. 2014; 124:32-55. – reference: Padgett WJ, Spurrier JD. Discrete failure models. IEEE Transactions on Reliability. 1985; 34(3):253-256. – reference: Pólya G. Sur quelques points de la théorie des probabilités (in French). Annales de l'institut Henri Poincaré. 1930; 2(1):117-167. – reference: Bracquemond C, Gaudoin O. A survey on discrete lifetime distributions. International Journal on Reliability, Quality, and Safety Engineering. 2003; 10(1):69-98. – reference: Lai CD. Constructions and applications of lifetime distributions. Applied Stochastic Models in Business and Industry. 2013; 29(2):127-140. – reference: Sheu SH, Griffith WS. Optimal number of minimal repairs before replacement of a system subject to shocks. Naval Research Logistics. 1996; 43:319-333. – reference: Lord D, Guikema SD, Geedipally SR. Application of the Conway-Maxwell-Poisson generalized linear model for analyzing motor vehicle crashes. Accident Analysis and Prevention. 2008; 40:1123-1134. – reference: Wein LM, Wu JT. Estimation of replicative senescence via a population dynamics model of cells in culture. Experimental Gerontology. 2001; 36(1):79-88. – reference: Roy D, Gupta RP. Classifications of discrete lives. Microelectronics and Reliability. 1992; 10(32):1450-1459. – reference: Barbour AD, Chryssaphinou O. Compound Poisson approximation: a user's guide. The Annals of Applied Probability. 2001; 11(3):964-1002. – reference: Eggenberger F, Pólya G. 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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 |
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