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

Edilson M. Assis (Department of Production Engineering, Federal University of Bahia, Salvador, Brazil)
Ernesto P. Borges (Federal University of Bahia, Salvador, Brazil. AND National Institute of Science and Technology for Complex Systems, Federal University of Bahia, Salvador, Brazil)
Silvio A.B. Vieira de Melo (Department of Production Engineering, Federal University of Bahia, Salvador, Brazil)
Leizer Schnitman (Department of Production Engineering, Federal University of Bahia, Salvador, Brazil)

International Journal of Quality & Reliability Management

ISSN: 0265-671X

Article publication date: 2 February 2015

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.

Keywords

Citation

Assis, E.M., Borges, E.P., Vieira de Melo, S.A.B. and Schnitman, L. (2015), "Modeling failure rate of a robotic welding station using generalized q-distributions", International Journal of Quality & Reliability Management, Vol. 32 No. 2, pp. 156-166. https://doi.org/10.1108/IJQRM-11-2012-0151

Publisher

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Emerald Group Publishing Limited

Copyright © 2015, Emerald Group Publishing Limited