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Mixture models for analyzing product reliability data: a case study

Overview of attention for article published in SpringerPlus, October 2015
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Title
Mixture models for analyzing product reliability data: a case study
Published in
SpringerPlus, October 2015
DOI 10.1186/s40064-015-1420-x
Pubmed ID
Authors

S. Ruhi, S. Sarker, M. R. Karim

Abstract

In the case of manufactured products, there are situations where some components of a product are produced over a period of time by collecting items from different vendors, using different raw materials, machines, and manpower. The physical characteristics and the reliabilities of such components may be different, but sometimes it is difficult to distinguish them clearly. In such situations, mixtures of distributions are often used in the analysis of reliability data for these components. Here a twofold Weibull-Weibull mixture model is applied to analyze product reliability data that consist of both failure and censored lifetimes. The Expectation-Maximization (EM) algorithm is used to find the maximum likelihood estimates of the model parameters. As a case study, it analyses an Aircraft component (Windshield) failure data and various characteristics of the mixture model, such as the reliability function, B10 life, mean time to failure, etc., are estimated to assess the reliability of the component. Simulation studies are performed to investigate the properties and uses of the proposed method.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 6%
Unknown 16 94%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 24%
Other 2 12%
Student > Bachelor 2 12%
Student > Ph. D. Student 2 12%
Professor 1 6%
Other 2 12%
Unknown 4 24%
Readers by discipline Count As %
Engineering 5 29%
Mathematics 4 24%
Business, Management and Accounting 1 6%
Environmental Science 1 6%
Materials Science 1 6%
Other 1 6%
Unknown 4 24%