The reliability analysis based on the generalized intuitionistic fuzzy two-parameter Pareto distribution

Roohanizadeh, Zahra; Jamkhaneh, Ezzatallah Baloui; Deiri, Einolah

doi:10.1007/s00500-022-07494-x

Cited by 9 publications

(2 citation statements)

References 29 publications

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“…They used this new type of GIFS to create reliability characteristics such as reliability and hazard functions for various life distributions such as exponential, Rayleigh, and so on. In their most recent work, Roohanizadeh, Z. and Baloui Jamkhaneh [35,36] presented different reliability characteristics specially designed for a two-parameter Pareto distribution using generalized intuitionistic fuzzy sets. The available literature makes it abundantly evident that understanding fuzzy reliability for fuzzy life distribution is critical from both a theoretical and a real-world perspective.…”

Section: Introductionmentioning

confidence: 99%

Statistical Fuzzy Reliability Analysis: An Explanation with Generalized Intuitionistic Fuzzy Lomax Distribution

Kalam,

Cheng,

et al. 2023

Symmetry

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To illustrate data uncertainty, intuitionistic fuzzy sets simply use membership and non-membership degrees. However, in some cases, a more complex strategy is required to deal with imprecise data. One of these techniques is generalized intuitionistic fuzzy sets (GIFSs), which provide a comprehensive framework by adding extra factors that provide a more realistic explanation for uncertainty. GIFSs contain generalized membership, non-membership, and hesitation degrees for establishing symmetry around a reference point. In this paper, we applied a generalized intuitionistic fuzzy set approach to investigate ambiguity in the parameter of the Lomax life distribution, seeking a more symmetric assessment of the reliability measurements. Several reliability measurements and associated cut sets for a novel L-R type fuzzy sets are derived after establishing the scale parameter as a generalized intuitionistic fuzzy number. Additionally, the study includes a range of reliability measurements, such as odds, hazards, reliability functions, etc., that are designed for the Lomax distribution within the framework of generalized intuitionistic fuzzy sets. These reliability measurements are an essential tool for evaluating the reliability characteristics of various types of complex systems. For the purpose of interpretation and application, the results are visually displayed and compared across different cut set values using a numerical example.

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Section: Introductionmentioning

confidence: 99%

Statistical Fuzzy Reliability Analysis: An Explanation with Generalized Intuitionistic Fuzzy Lomax Distribution

Kalam,

Cheng,

et al. 2023

Symmetry

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“…When dealing with decision-making problems, intuitionistic fuzzy sets can provide more information, making the decision results more accurate and reliable, and have a wider range of application prospects [22][23][24][25][26]. Zahra et al [27] considered the parameter and reliability estimation of the Pareto distribution by setting the parameter as a generalized intuitionistic fuzzy number. First, an L-R-type intuitionistic fuzzy number was proposed, and its cut set was provided.…”

Section: Introductionmentioning

confidence: 99%

Reliability Estimation of Inverse Weibull Distribution Based on Intuitionistic Fuzzy Lifetime Data

Hu,

Ren

2023

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As a commonly used model in reliability analysis, the inverse Weibull distribution (IWD) is widely applied in various scientific fields. This paper considers the reliability estimation of the IWD based on intuitionistic fuzzy lifetime data. Firstly, the related concepts of the fuzzy set theory are reviewed, and the concepts of the intuitionistic fuzzy conditional density, intuitionistic fuzzy likelihood function, and intuitionistic fuzzy conditional expectation are obtained by extension. In classical estimations, the maximum likelihood estimators of parameters and reliability are derived. Due to the nonlinearity, the EM algorithm is used to obtain the maximum likelihood estimates. In the Bayesian estimation, the gamma prior is selected, and the Bayesian estimation of the parameters and reliability is conducted under the symmetric entropy and the scale square error loss function, respectively. Since the integrals are complicated, the Lindley approximation is used to approximate the Bayesian estimates. Then, the performance of these estimators is evaluated by the Monte Carlo simulation. The simulation results show that the Bayesian estimation is more suitable than the maximum likelihood estimation for the reliability estimation. Finally, a set of real data is used to prove the effectiveness of these proposed methods. Through these methods, the reliability of the intuitive fuzzy life data is accurately estimated, which provides an important reference for the reliability analysis in the scientific field.

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Fuzzy reliability framework under hesitant and dual hesitant fuzzy sets to air conditioning system

Chachra,

Ram,

Kumar

2023

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The reliability analysis based on the generalized intuitionistic fuzzy two-parameter Pareto distribution

Cited by 9 publications

References 29 publications

Statistical Fuzzy Reliability Analysis: An Explanation with Generalized Intuitionistic Fuzzy Lomax Distribution

Statistical Fuzzy Reliability Analysis: An Explanation with Generalized Intuitionistic Fuzzy Lomax Distribution

Reliability Estimation of Inverse Weibull Distribution Based on Intuitionistic Fuzzy Lifetime Data

Fuzzy reliability framework under hesitant and dual hesitant fuzzy sets to air conditioning system

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