Estimating the parameters of 3-p Weibull distribution using particle swarm optimization: A comprehensive experimental comparison

Örkçü, H. Hasan; Özsoy, Volkan Soner; Aksoy, Ertuğrul; Doğan, Mustafa

doi:10.1016/j.amc.2015.06.043

Cited by 28 publications

(14 citation statements)

References 47 publications

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“…The scale parameter (ŋ), is also known as the characteristic life parameter or characteristic time. The location parameter (ϒ) assists in locating the points on the abscissa where the function starts [9,18,20].…”

Section: Calculation Of Replacement Policy Comparison Parametersmentioning

confidence: 99%

Optimization of the replacement time for critical repairable components

Torres¹,

Oblitas²,

Castro³

2020

DYNA

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Equipment-intensive industries must manage critical components due to their impact on the availability and high inventory carrying costs. In this context, this study seeks to assess mean times between interventions (MTBI) and mean times between failures (MTBF) to determine optimal replacement times for critical repairable components used in six EX5500 hydraulic excavators operating at an open-pit mining site. For these purposes, the authors compared a base policy using the MTBF values provided by the equipment manufacturer, against the proposed policy using the MTBI values obtained from equipment intervention records. The results from the study, revealed that the MTBI policy was able to streamline the replacement times for critical repairable components, thus, generating a cost optimization model at a higher level of reliability

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Section: Calculation Of Replacement Policy Comparison Parametersmentioning

confidence: 99%

Optimization of the replacement time for critical repairable components

Torres¹,

Oblitas²,

Castro³

2020

DYNA

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“…Hence, it is mandatory to apply a heuristics-based optimization method (Handoyo et al, 2017). Heuristic optimization methods such as simulated annealing (SANN) and PSO have been used to the likelihood function of statistical distributions (Abbasi et al, 2006;Örkcü et al, 2015). Do Nascimento et al, (2020a) compared the adjustments made between the Weibull distribution with the Method of Research, Society and Development, v. 9, n. 8, e444985841, 2020 (CC BY 4.0) | ISSN 2525-3409 | DOI: http://dx.doi.org/10.33448/rsd-v9i8.5841…”

Section: Introductionmentioning

confidence: 99%

An application of Particle Swarm Optimization (PSO) algorithm with daily precipitation data in Campina Grande, Paraíba, Brazil

Júnior

Xavier

Jale

et al. 2020

RSD

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We study the daily precipitation in the municipality of Campina Grande, estimating the parameters of Gamma, Log-Normal, and Weibull distributions. To evaluate the parameter estimators, we compared the Particle Swarm Optimization (PSO) versus Maximum Likelihood Estimation (MLE) to analyze and understand the behaviour of the daily precipitation in Campina Grande. In most cases, our results show evidence that the PSO algorithm is an efficient and robust technique. Notwithstanding, the algorithm also presents an efficient parameter estimation due to its fast convergence.

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“…Usta et al [27] combined the probability weighted moments and the power density method for estimating the Weibull parameters. In [28], the particle swarm optimization (PSO) was adopted to provide accurate estimations of the Weibull parameters. Petkovic et al [29] proposed an adaptive neuro-fuzzy inference system to predict the parameters of the Weibull distribution.…”

Section: Introductionmentioning

confidence: 99%

Maximum Likelihood Estimation for Three‐Parameter Weibull Distribution Using Evolutionary Strategy

Yang

Ren

2019

Mathematical Problems in Engineering

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The maximum likelihood estimation is a widely used approach to the parameter estimation. However, the conventional algorithm makes the estimation procedure of three-parameter Weibull distribution difficult. Therefore, this paper proposes an evolutionary strategy to explore the good solutions based on the maximum likelihood method. The maximizing process of likelihood function is converted to an optimization problem. The evolutionary algorithm is employed to obtain the optimal parameters for the likelihood function. Examples are presented to demonstrate the proposed method. The results show that the proposed method is suitable for the parameter estimation of the three-parameter Weibull distribution.

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Estimating the parameters of 3-p Weibull distribution using particle swarm optimization: A comprehensive experimental comparison

Cited by 28 publications

References 47 publications

Optimization of the replacement time for critical repairable components

Optimization of the replacement time for critical repairable components

An application of Particle Swarm Optimization (PSO) algorithm with daily precipitation data in Campina Grande, Paraíba, Brazil

Maximum Likelihood Estimation for Three‐Parameter Weibull Distribution Using Evolutionary Strategy

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