Estimation Study of Multicomponent Stress-Strength Reliability Using Advanced Sampling Approach

Hassan, Amal S.; Elshaarawy, Rasha S.; Nagy, Heba F.

doi:10.35378/gujs.1132770

Cited by 3 publications

(3 citation statements)

References 43 publications

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“…We will be considering the reliability of a generalized stress-strength model in a future paper that consists of a serial system with one stress and multiple strengths. For more examples, see [20,[49][50][51][52][53].…”

Section: Discussionmentioning

confidence: 99%

“…Some extended forms for ILo distribution have been presented (see, for example, [14][15][16][17][18]). The SS (stress-strength) reliability estimator of the ILo model, based on extreme and median ranked set sampling, has been presented, respectively, by Al-Omari et al [19] and Hassan et al [20]. The CDF (cumulative distribution function) of a two-parameter ILo distribution, with shape parameter ω and scale parameter ρ, is specified by the following:…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Engineering Applications with Stress-Strength for a New Flexible Extension of Inverse Lomax Model: Bayesian and Non-Bayesian Inference

Alyami,

Elbatal,

Hassan

et al. 2023

Axioms

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In this paper, we suggest a brand new extension of the inverse Lomax distribution for fitting engineering time data. The newly developed distribution, termed the transmuted Topp–Leone inverse Lomax (TTLILo) distribution, is characterized by an additional shape and transmuted parameters. It is critical to notice that the skewness, kurtosis, and tail weights of the distribution are strongly influenced by these additional characteristics of the extra parameters. The TTLILo model is capable of producing right-skewed, J-shaped, uni-modal, and reversed-J-shaped densities. The proposed model’s statistical characteristics, including the moments, entropy values, stochastic ordering, stress-strength model, incomplete moments, and quantile function, are examined. Moreover, characterization based on two truncated moments is offered. Using Bayesian and non-Bayesian estimating techniques, we estimate the distribution parameters of the suggested distribution. The bootstrap procedure, approximation, and Bayesian credibility are the three forms of confidence intervals that have been created. A simulation study is used to assess the efficiency of the estimated parameters. The TTLILo model is then put to the test by being applied to actual engineering datasets, demonstrating that it offers a good match when compared to alternative models. Two applications based on real engineering datasets are taken into consideration: one on the failure times of airplane air conditioning systems and the other on the active repair times of airborne communication transceivers. Also, we consider the problem of estimating the stress-strength parameter R=P(Z2<Z1) with engineering application.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Engineering Applications with Stress-Strength for a New Flexible Extension of Inverse Lomax Model: Bayesian and Non-Bayesian Inference

Alyami,

Elbatal,

Hassan

et al. 2023

Axioms

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“…[17,18]. For more research on RSS-based reliability estimate [19,20,21,22,23]. Further findings on RSS-based parametric estimation encompass several estimation techniques [24,25,26,27,28,29].…”

Section: Introductionmentioning

confidence: 99%

Optimal Estimation of Power Chris-Jerry DistributionParameters Using Ranked Set Sampling Design withApplication

Alsadat,

Hassan,

Elgarhy

et al. 2024

Preprint

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Effective sample design has a major role in the quality of parameter estimation in statisticalparameter estimation issues. The ranking set sampling (RSS) strategy is effective and a less costlyoption than simple random sampling (SRS). A novel mixture continuous lifetime distribution thathas been proposed recently is the power Chris-Jerry distribution (PC-JD). It is useful for modelinga number of real data sets. This paper investigates the RSS approach for estimating the PC-JD’sparameters. There are roughly sixteen different techniques of estimation that are used, such as themaximum likelihood method, the percentiles method, some methods based on minimum distance,the Kolmogorov method, and some methods based on minimum and maximum spacing distances. Incomparison to a SRS, the simulation research assesses the performance of the suggested RSS-basedestimates in terms of some measures of accuracy. To identify the optimal estimating strategy, thepartial and overall ranks of many estimates are shown. According to numerical results, the maximumlikelihood approach seems to be quite beneficial in evaluating the estimated quality of RSS and SRS.RSS is a more effective sampling approach than SRS owing to its better efficiency. Additionally, thedifferent estimation techniques with survival data for both sampling techniques are examined

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On Estimating Multi- Stress Strength Reliability for Inverted Kumaraswamy Under Ranked Set Sampling with Application in Engineering

Hassan,

Alsadat,

Elgarhy

et al. 2024

J Nonlinear Math Phys

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The terrible operating constraints of many real-world events cause systems to malfunction regularly. The failure of systems to perform their intended duties when they reach their lowest, highest, or both extreme operating conditions is a phenomenon that researchers rarely focus on. The multi-stress strength reliability $$R = P(W<X<Z)$$ R = P ( W < X < Z ) is deemed in this study for a component whose strength X falls between two stresses, W, and Z, where X, W, and Z are independently inverted Kumaraswamy distributed. Both maximum likelihood and maximum product spacing procedures are employed to obtain the reliability estimator under simple random sampling (SRS) and ranked set sampling (RSS) methodologies. Four scenarios for reliability estimators are considered. The reliability estimator in the first and second cases can be determined by applying the same sample design (RSS/SRS) to the strength and stress distributions. When the sample data for W and Z originate from RSS while those for X are acquired from SRS, the third reliability estimator is calculated. The drawn data of the strength and stress random variables, which are obtained from SRS and RSS, respectively, are taken into consideration in the final scenario. The effectiveness of the suggested estimators is compared using a comprehensive computer simulation. Lastly, three real data sets have been used to determine reliability estimators.

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Estimation Study of Multicomponent Stress-Strength Reliability Using Advanced Sampling Approach

Cited by 3 publications

References 43 publications

Engineering Applications with Stress-Strength for a New Flexible Extension of Inverse Lomax Model: Bayesian and Non-Bayesian Inference

Engineering Applications with Stress-Strength for a New Flexible Extension of Inverse Lomax Model: Bayesian and Non-Bayesian Inference

Optimal Estimation of Power Chris-Jerry DistributionParameters Using Ranked Set Sampling Design withApplication

On Estimating Multi- Stress Strength Reliability for Inverted Kumaraswamy Under Ranked Set Sampling with Application in Engineering

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