{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T20:28:34Z","timestamp":1768681714434,"version":"3.49.0"},"reference-count":39,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,6,12]],"date-time":"2023-06-12T00:00:00Z","timestamp":1686528000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Axioms"],"abstract":"<jats:p>In this paper, fuzzy stress strengths RF=P(Y\u227aX) and traditional stress strengths R=P(Y&lt;X) are considered and compared when X and Y are independently inverse Weibull random variables. When axiomatic fuzzy set theory is taken into account in the stress\u2013strength inference, it enables the generation of more precise studies on the underlying systems. We discuss estimating both conventional and fuzzy models of stress strength utilizing a maximum product of spacing, maximum likelihood, and Bayesian approaches. Simulations based on the Markov Chain Monte Carlo method are used to produce various estimators of conventional and fuzzy dependability of stress strength for the inverse Weibull model. To generate both conventional and fuzzy models of dependability, we use the Metropolis\u2013Hastings method while performing Bayesian estimation. In conclusion, we will examine a scenario taken from actual life and apply a real-world data application to validate the accuracy of the provided estimators.<\/jats:p>","DOI":"10.3390\/axioms12060582","type":"journal-article","created":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T02:56:34Z","timestamp":1686624994000},"page":"582","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Fuzzy vs. Traditional Reliability Model for Inverse Weibull Distribution"],"prefix":"10.3390","volume":"12","author":[{"given":"Eslam","family":"Hussam","sequence":"first","affiliation":[{"name":"Department of Mathematics, Faculty of Science, Helwan University, Cairo 12613, Egypt"}]},{"given":"Mohamed A.","family":"Sabry","sequence":"additional","affiliation":[{"name":"Department of Mathematical Statistics, Faculty of Graduate Studies for Statistical Research, Cairo University, Cairo 12613, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3608-8449","authenticated-orcid":false,"given":"M. M.","family":"Abd El-Raouf","sequence":"additional","affiliation":[{"name":"Basic and Applied Science Institute, Arab Academy for Science, Technology and Maritime Transport (AASTMT), Alexandria 21599, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3888-1275","authenticated-orcid":false,"given":"Ehab M.","family":"Almetwally","sequence":"additional","affiliation":[{"name":"Faculty of Business Administration, Delta University for Science and Technology, Gamasa 11152, Egypt"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.1016\/0026-2714(94)00173-L","article-title":"Reliability analysis method in the presence of fuzziness attached to operating time","volume":"35","author":"Huang","year":"1995","journal-title":"Microelectron. Reliab."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Deep, K., Jain, M., and Salhi, S. (2018). 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