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Bayesian Point Estimation of Linear/Circular Consecutive k-out-of-n:F System Reliability Under Vague Environment.

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Title: Bayesian Point Estimation of Linear/Circular Consecutive k-out-of-n:F System Reliability Under Vague Environment.
Authors: Madhumitha, J.1 (AUTHOR) madhumij@srmist.edu.in, Vijayalakshmi, G.2 (AUTHOR) vijayalgsrm@gmail.com
Source: International Journal of Reliability, Quality & Safety Engineering. Feb2025, Vol. 32 Issue 1, p1-25. 25p.
Subjects: Reliability in engineering, Fix-point estimation, Error functions, Random variables, Information storage & retrieval systems
Abstract: The paper proposes a Bayesian approach to estimate the reliability and mean time to failure (MTTF) of linear and circular consecutive k -out-of- n : F systems under vague environments, where uncertainties are represented using vague sets. By assuming model parameters as vague random variables with vague prior distributions, the study employs the Resolution Identity theorem for vague sets to resolve and structure this vagueness within the Bayesian framework. The proposed method uses the squared error loss function to derive vague Bayesian estimates, providing a more realistic and comprehensive analysis of system reliability in the presence of uncertainty. Numerical illustrations are included to demonstrate the applicability and effectiveness of the approach, highlighting its potential for handling vague information in complex system reliability analysis. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Reliability, Quality & Safety Engineering is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Reliability%2C+Quality+%26+Safety+Engineering%22">International Journal of Reliability, Quality & Safety Engineering</searchLink>. Feb2025, Vol. 32 Issue 1, p1-25. 25p.
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  Data: <searchLink fieldCode="DE" term="%22Reliability+in+engineering%22">Reliability in engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Fix-point+estimation%22">Fix-point estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Error+functions%22">Error functions</searchLink><br /><searchLink fieldCode="DE" term="%22Random+variables%22">Random variables</searchLink><br /><searchLink fieldCode="DE" term="%22Information+storage+%26+retrieval+systems%22">Information storage & retrieval systems</searchLink>
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  Data: The paper proposes a Bayesian approach to estimate the reliability and mean time to failure (MTTF) of linear and circular consecutive k -out-of- n : F systems under vague environments, where uncertainties are represented using vague sets. By assuming model parameters as vague random variables with vague prior distributions, the study employs the Resolution Identity theorem for vague sets to resolve and structure this vagueness within the Bayesian framework. The proposed method uses the squared error loss function to derive vague Bayesian estimates, providing a more realistic and comprehensive analysis of system reliability in the presence of uncertainty. Numerical illustrations are included to demonstrate the applicability and effectiveness of the approach, highlighting its potential for handling vague information in complex system reliability analysis. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Reliability, Quality & Safety Engineering is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Error functions
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              Text: Feb2025
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