Weibull Decision Support Systems in Maintenance

• Authors: Khalid Aboura , Johnson I. Agbinya , Ali Eskandarian
• Languages of publication: EN

Abstracts

EN

Background: The Weibull distribution is one of the most important lifetime distributions in applied statistics. Weibull analysis is the leading method in the world for fitting and analyzing lifetime data. We discuss one of the earliest decision support system for the assessment of a distribution for the parameters of the Weibull reliability model using expert information. We then present a different approach to assess the parameters distribution. Objectives: The studies mentioned in this paper aimed to construct a distribution of the parameters of the Weibull reliability model and apply it in the domain of Maintenance Optimization. Method: The parameters of the Weibull reliability model are considered random variables and a distribution for the parameters is assessed using informed judgment in the form of reliability estimates from vendor information, engineering knowledge or experience in the field. Results: The results are the development of modern maintenance optimization models that can be embodied in decision support systems. Conclusion: While the information management part is important in the building of maintenance optimization decision systems, the accuracy of the mathematical and statistical algorithms determines the level of success of the maintenance solution.

Keywords

EN

Weibull Distribution; Reliability; Inference; Maintenance; Expert Opinion

• Publisher: Sciendo
• Journal: Organizacija
• Year: 2014
• Volume: 47
• Issue: 2
• Pages: 81-89

Dates

published

2014-05-01

received

2013-11-04

revised

2014-01-16

accepted

2014-02-09

online

2014-05-17

Contributors

author

Khalid Aboura

• University of Dammam, College of Business Administration, Dammam, Saudi Arabia

author

Johnson I. Agbinya

• La Trobe University, Faculty of Science, Technology and Engineering School of Engineering and Mathematical Sciences, Department of Electronic Engineering, Melbourne, Australia

author

Ali Eskandarian

• George Washington University, Department of Physics, 805 21st street, NW, suite 301,Washington D.C., U.S.A

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Identifiers

DOI

10.2478/orga-2014-0008