Multicomponent Stress–Strength Reliability with Extreme Value Distribution Margins: Its Theory and Application to Hydrological Data
This paper focuses on the estimation of multicomponent stress–strength models, an important concept in reliability analyses used to determine the probability that a system will function successfully under varying stress conditions. Understanding and accurately estimating these probabilities is essen...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
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| Series: | J |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2571-8800/7/4/32 |
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| Summary: | This paper focuses on the estimation of multicomponent stress–strength models, an important concept in reliability analyses used to determine the probability that a system will function successfully under varying stress conditions. Understanding and accurately estimating these probabilities is essential in fields such as engineering and risk management, where the reliability of components under extreme conditions can have significant consequences. This is the case in applications where one seeks to model extreme hydrological events. Specifically, this study examines cases where the random variables <i>X</i> (representing strength) and <i>Y</i> (representing stress) follow extreme value distributions. New analytical expressions are derived for multicomponent stress–strength reliability (MSSR) when different classes of extreme distributions are considered, using the extreme value <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="double-struck">H</mi></semantics></math></inline-formula>-function. These results are applied to three <i>l</i>-max stable laws and six <i>p</i>-max stable laws, providing a robust theoretical framework for multicomponent stress–strength analyses under extreme conditions. To demonstrate the practical relevance of the proposed models, a real dataset is analyzed, focusing on the monthly water capacity of the Shasta Reservoir in California (USA) during August and December from 1980 to 2015. This application showcases the effectiveness of the derived expressions in modeling real-world data. |
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| ISSN: | 2571-8800 |