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Statistical inferences under step stress partially accelerated life testing based on multiple censoring approaches using simulated and real-life engineering data

Evaluating the lifespan distribution of highly reliable commodities under regular use is exceedingly difficult, time consuming, and extremely expensive. As a result of its ability to provide more failure data faster and at a lower experimental cost, accelerated life testing has become increasingly i...

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Autores principales: Rahman, Ahmadur, Kamal, Mustafa, Khan, Shahnawaz, Khan, Mohammad Faisal, Mustafa, Manahil SidAhmed, Hussam, Eslam, Atchadé, Mintodê Nicodème, Al Mutairi, Aned
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10394031/
https://www.ncbi.nlm.nih.gov/pubmed/37528103
http://dx.doi.org/10.1038/s41598-023-39170-x
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author Rahman, Ahmadur
Kamal, Mustafa
Khan, Shahnawaz
Khan, Mohammad Faisal
Mustafa, Manahil SidAhmed
Hussam, Eslam
Atchadé, Mintodê Nicodème
Al Mutairi, Aned
author_facet Rahman, Ahmadur
Kamal, Mustafa
Khan, Shahnawaz
Khan, Mohammad Faisal
Mustafa, Manahil SidAhmed
Hussam, Eslam
Atchadé, Mintodê Nicodème
Al Mutairi, Aned
author_sort Rahman, Ahmadur
collection PubMed
description Evaluating the lifespan distribution of highly reliable commodities under regular use is exceedingly difficult, time consuming, and extremely expensive. As a result of its ability to provide more failure data faster and at a lower experimental cost, accelerated life testing has become increasingly important in life testing studies. In this article, we concentrate on parametric inference for step stress partially life testing utilizing multiple censored data based on the Tampered Random Variable model. Under normal stress circumstances, the lifespan of the experimental units is assumed to follow the Nadarajah–Haghighi distribution, with and being the shape and scale parameters, respectively. Maximum likelihood estimates for model parameters and acceleration factor are developed using multiple censored data. We build asymptotic confidence intervals for the unknown parameters using the observed Fisher information matrix. To demonstrate the applicability of the different methodologies, an actual data set based on the timings of subsequent failures of consecutive air conditioning system failures for each member of a Boeing 720 jet aircraft fleet is investigated. Finally, thorough simulation studies utilizing various censoring strategies are performed to evaluate the estimate procedure performance. Several sample sizes were studied in order to investigate the finite sample features of the considered estimators. According to our numerical findings, the values of mean squared errors and average asymptotic confidence intervals lengths drop as sample size increases. Furthermore, when the censoring level is reduced, the considered estimates of the parameters approach their genuine values.
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spelling pubmed-103940312023-08-03 Statistical inferences under step stress partially accelerated life testing based on multiple censoring approaches using simulated and real-life engineering data Rahman, Ahmadur Kamal, Mustafa Khan, Shahnawaz Khan, Mohammad Faisal Mustafa, Manahil SidAhmed Hussam, Eslam Atchadé, Mintodê Nicodème Al Mutairi, Aned Sci Rep Article Evaluating the lifespan distribution of highly reliable commodities under regular use is exceedingly difficult, time consuming, and extremely expensive. As a result of its ability to provide more failure data faster and at a lower experimental cost, accelerated life testing has become increasingly important in life testing studies. In this article, we concentrate on parametric inference for step stress partially life testing utilizing multiple censored data based on the Tampered Random Variable model. Under normal stress circumstances, the lifespan of the experimental units is assumed to follow the Nadarajah–Haghighi distribution, with and being the shape and scale parameters, respectively. Maximum likelihood estimates for model parameters and acceleration factor are developed using multiple censored data. We build asymptotic confidence intervals for the unknown parameters using the observed Fisher information matrix. To demonstrate the applicability of the different methodologies, an actual data set based on the timings of subsequent failures of consecutive air conditioning system failures for each member of a Boeing 720 jet aircraft fleet is investigated. Finally, thorough simulation studies utilizing various censoring strategies are performed to evaluate the estimate procedure performance. Several sample sizes were studied in order to investigate the finite sample features of the considered estimators. According to our numerical findings, the values of mean squared errors and average asymptotic confidence intervals lengths drop as sample size increases. Furthermore, when the censoring level is reduced, the considered estimates of the parameters approach their genuine values. Nature Publishing Group UK 2023-08-01 /pmc/articles/PMC10394031/ /pubmed/37528103 http://dx.doi.org/10.1038/s41598-023-39170-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Rahman, Ahmadur
Kamal, Mustafa
Khan, Shahnawaz
Khan, Mohammad Faisal
Mustafa, Manahil SidAhmed
Hussam, Eslam
Atchadé, Mintodê Nicodème
Al Mutairi, Aned
Statistical inferences under step stress partially accelerated life testing based on multiple censoring approaches using simulated and real-life engineering data
title Statistical inferences under step stress partially accelerated life testing based on multiple censoring approaches using simulated and real-life engineering data
title_full Statistical inferences under step stress partially accelerated life testing based on multiple censoring approaches using simulated and real-life engineering data
title_fullStr Statistical inferences under step stress partially accelerated life testing based on multiple censoring approaches using simulated and real-life engineering data
title_full_unstemmed Statistical inferences under step stress partially accelerated life testing based on multiple censoring approaches using simulated and real-life engineering data
title_short Statistical inferences under step stress partially accelerated life testing based on multiple censoring approaches using simulated and real-life engineering data
title_sort statistical inferences under step stress partially accelerated life testing based on multiple censoring approaches using simulated and real-life engineering data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10394031/
https://www.ncbi.nlm.nih.gov/pubmed/37528103
http://dx.doi.org/10.1038/s41598-023-39170-x
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