Determining the optimal maintenance action for a deteriorating repairable system

Kim, T.S.; Park, C.S.; Ahn, Suneung

doi:10.1016/j.probengmech.2007.12.003

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…The Bayes estimation is one of the best methods for estimating the parameters of stochastic processes due to its accuracy. This method depends on the prior distribution of parameters and the information resulting from the sample about the parameter that is obtained from the maximum likelihood function in order to get the posterior distribution of the function [15]. From the likelihood function (7), we assume that the prior distribution for each parameter follows the gamma distribution [16]: , , The probability density function for each parameter is given as follows:…”

Section: Bayes Estimation (Bay)mentioning

confidence: 99%

Bayesian Estimation of Power Law Function in Non-homogeneous Poisson Process Applied in Mosul Gas Power Plant – Iraq

Alsultan,

Sulaiman

2024

Iraqi Journal of Science

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Non-homogeneous Poisson process with power law intensity function has often been used as a model for describing the failure pattern of repairable systems. Maximum likelihood and Bayesian estimation are used to estimate model parameters. Simulation and realistic application are used and represented by shutting down the gas power plant in Mosul. Stops in hours are designed with the power law random process model in order to obtain a model that represents the average stop time of the units throughout the study period in the best way. The results of the application on the data of the three concerned stations show that the Bayes estimate is better than the maximum likelihood estimate. This proves that the Bayes methods are very accurate and effective in estimating the rate of occurrence parameters.

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Section: Bayes Estimation (Bay)mentioning

confidence: 99%

Bayesian Estimation of Power Law Function in Non-homogeneous Poisson Process Applied in Mosul Gas Power Plant – Iraq

Alsultan,

Sulaiman

2024

Iraqi Journal of Science

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“…Plusieurs autres techniques de représentation pourraient être utilisées (Arbre de défaillance, logique floue, système expert, arbre de décision…), mais la préférence donnée aux RBs tient plus particulièrement à leur polyvalence. En l'occurrence, nous pouvons se servir du même modèle pour diagnostiquer (conséquences -causes) [9], ou faire des analyses prédictives (causes -conséquences) [10].…”

Section: Réseaux Bayésiensunclassified

Fault diagnosis of electrical power transformer based on water content analysis using Bayesian network

Lakehal¹,

Chouaki²,

Ghemari

et al. 2014

2014 International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM)

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Water content and breakdown voltage of dielectric oil are generally unstable parameters. Exceeding limit permissible threshold of one of parameters implies corrective actions because they are directly related to the oil ability to isolate. In this paper a model based on a Bayesian network (BN) is used to diagnose the causes of transformer failures. The proposed model is used to diagnose the water content in the oil, and to predict the breakdown voltage. A case study of a main transformer (MT) of a power plant is presented to show the effectiveness of our model.

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“…There are some disadvantages in the frequentist context, since the estimators under the MLEs are biased for small samples and the confidence intervals relies in the asymptotic theory, returning non reliable results. To overcome this problem Bayesian inference can be used, several papers have considered this approach, such as Guida et al [ 15 ] who used several choices of informative and noninformative priors, Sen [ 16 ], Yu et al [ 17 ] and de Oliveira et al [ 18 ] that use non-informative prior, and as Kim et al [ 19 ] and Huang [ 20 ] that use conjugate prior distribution. dos Reis et.…”

Section: Introductionmentioning

confidence: 99%

Objective bayesian analysis for multiple repairable systems

et al. 2021

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This article focus on the analysis of the reliability of multiple identical systems that can have multiple failures over time. A repairable system is defined as a system that can be restored to operating state in the event of a failure. This work under minimal repair, it is assumed that the failure has a power law intensity and the Bayesian approach is used to estimate the unknown parameters. The Bayesian estimators are obtained using two objective priors know as Jeffreys and reference priors. We proved that obtained reference prior is also a matching prior for both parameters, i.e., the credibility intervals have accurate frequentist coverage, while the Jeffreys prior returns unbiased estimates for the parameters. To illustrate the applicability of our Bayesian estimators, a new data set related to the failures of Brazilian sugar cane harvesters is considered.

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Determining the optimal maintenance action for a deteriorating repairable system

Cited by 8 publications

References 11 publications

Bayesian Estimation of Power Law Function in Non-homogeneous Poisson Process Applied in Mosul Gas Power Plant – Iraq

Bayesian Estimation of Power Law Function in Non-homogeneous Poisson Process Applied in Mosul Gas Power Plant – Iraq

Fault diagnosis of electrical power transformer based on water content analysis using Bayesian network

Objective bayesian analysis for multiple repairable systems

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