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Reliability analysis of the triple modular redundancy system under step-partially accelerated life tests using Lomax distribution
Triple modular redundancy (TMR) is a robust technique utilized in safety-critical applications to enhance fault-tolerance and reliability. This article focuses on estimating the distribution parameters of a TMR system under step-stress partially accelerated life tests, where each component included...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10485010/ https://www.ncbi.nlm.nih.gov/pubmed/37679416 http://dx.doi.org/10.1038/s41598-023-41363-3 |
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author | Al-Essa, Laila A. Abdel-Hamid, Alaa H. Alballa, Tmader Hashem, Atef F. |
author_facet | Al-Essa, Laila A. Abdel-Hamid, Alaa H. Alballa, Tmader Hashem, Atef F. |
author_sort | Al-Essa, Laila A. |
collection | PubMed |
description | Triple modular redundancy (TMR) is a robust technique utilized in safety-critical applications to enhance fault-tolerance and reliability. This article focuses on estimating the distribution parameters of a TMR system under step-stress partially accelerated life tests, where each component included in the system follows a Lomax distribution. The study aims to analyze the system’s reliability and mean residual lifetime based on the estimated parameters. Various estimation techniques, including maximum likelihood, percentile, least squares, and maximum product of spacings, are explored. Additionally, the optimal stress change time is determined using two criteria. An illustrative example supported by two actual data sets is presented to showcase the methodology’s application. By conducting Monte Carlo simulations, the assessment of the estimation methods’ effectiveness reveals that the maximum likelihood method outperforms the other three methods in terms of both accuracy and performance, as indicated by the numerical outcomes. This research contributes to the understanding and practical implementation of TMR systems in safety-critical industries, potentially saving lives and preventing catastrophic events. |
format | Online Article Text |
id | pubmed-10485010 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-104850102023-09-09 Reliability analysis of the triple modular redundancy system under step-partially accelerated life tests using Lomax distribution Al-Essa, Laila A. Abdel-Hamid, Alaa H. Alballa, Tmader Hashem, Atef F. Sci Rep Article Triple modular redundancy (TMR) is a robust technique utilized in safety-critical applications to enhance fault-tolerance and reliability. This article focuses on estimating the distribution parameters of a TMR system under step-stress partially accelerated life tests, where each component included in the system follows a Lomax distribution. The study aims to analyze the system’s reliability and mean residual lifetime based on the estimated parameters. Various estimation techniques, including maximum likelihood, percentile, least squares, and maximum product of spacings, are explored. Additionally, the optimal stress change time is determined using two criteria. An illustrative example supported by two actual data sets is presented to showcase the methodology’s application. By conducting Monte Carlo simulations, the assessment of the estimation methods’ effectiveness reveals that the maximum likelihood method outperforms the other three methods in terms of both accuracy and performance, as indicated by the numerical outcomes. This research contributes to the understanding and practical implementation of TMR systems in safety-critical industries, potentially saving lives and preventing catastrophic events. Nature Publishing Group UK 2023-09-07 /pmc/articles/PMC10485010/ /pubmed/37679416 http://dx.doi.org/10.1038/s41598-023-41363-3 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 Al-Essa, Laila A. Abdel-Hamid, Alaa H. Alballa, Tmader Hashem, Atef F. Reliability analysis of the triple modular redundancy system under step-partially accelerated life tests using Lomax distribution |
title | Reliability analysis of the triple modular redundancy system under step-partially accelerated life tests using Lomax distribution |
title_full | Reliability analysis of the triple modular redundancy system under step-partially accelerated life tests using Lomax distribution |
title_fullStr | Reliability analysis of the triple modular redundancy system under step-partially accelerated life tests using Lomax distribution |
title_full_unstemmed | Reliability analysis of the triple modular redundancy system under step-partially accelerated life tests using Lomax distribution |
title_short | Reliability analysis of the triple modular redundancy system under step-partially accelerated life tests using Lomax distribution |
title_sort | reliability analysis of the triple modular redundancy system under step-partially accelerated life tests using lomax distribution |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10485010/ https://www.ncbi.nlm.nih.gov/pubmed/37679416 http://dx.doi.org/10.1038/s41598-023-41363-3 |
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