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Modeling failure rate of a robotic welding station using generalized q-distributions

Modeling failure rate of a robotic welding station using generalized q-distributions Purpose – The purpose of this paper is to compare four life data models, namely the exponential and the Weibull models, and their corresponding generalized versions, q -exponential and q -Weibull models, by means of one practical application. Design/methodology/approach – Application of the models to a practical example (a welding station), with estimation of parameters by the use of the least squares method, and the Akaike Information Criterion (AIC). Findings – The data of the example considered in this paper is divided into three regimes, decreasing, constant and increasing failure rate, and the q -Weibull model describes the bathtub curve displayed by the data with a single set of parameters. Practical implications – The simplicity and flexibility of the q -Weibull model may be very useful for practitioners of reliability analysis, and its benefits surpasses the inconvenience of the additional parameter, as AIC shows. Originality/value – The q -Weibull model is compared in detail with other three models, through the analysis of one example that clearly exhibits a bathtub curve, and it is shown that it can describe the whole time range with a single set of parameters. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Quality & Reliability Management Emerald Publishing

Modeling failure rate of a robotic welding station using generalized q-distributions

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
0265-671X
DOI
10.1108/IJQRM-11-2012-0151
Publisher site
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Abstract

Purpose – The purpose of this paper is to compare four life data models, namely the exponential and the Weibull models, and their corresponding generalized versions, q -exponential and q -Weibull models, by means of one practical application. Design/methodology/approach – Application of the models to a practical example (a welding station), with estimation of parameters by the use of the least squares method, and the Akaike Information Criterion (AIC). Findings – The data of the example considered in this paper is divided into three regimes, decreasing, constant and increasing failure rate, and the q -Weibull model describes the bathtub curve displayed by the data with a single set of parameters. Practical implications – The simplicity and flexibility of the q -Weibull model may be very useful for practitioners of reliability analysis, and its benefits surpasses the inconvenience of the additional parameter, as AIC shows. Originality/value – The q -Weibull model is compared in detail with other three models, through the analysis of one example that clearly exhibits a bathtub curve, and it is shown that it can describe the whole time range with a single set of parameters.

Journal

International Journal of Quality & Reliability Management – Emerald Publishing

Published: Feb 2, 2015

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