• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.10 no.2
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• pp.41-47
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• 2006

The Renewal process and the Non-homogeneous Poisson process (NHPP) process are probably the most popular models for describing the failure pattern of repairable systems. But both these models are based on too restrictive assumptions on the effect of the repair action. For these reasons, several authors have recently proposed point process models which incorporate both renewal type behavior and time trend. One of these models is the Modulated Power Law Process (MPLP). The Modulated Power Law Process is a suitable model for describing the failure pattern of repairable systems when both renewal-type behavior and time trend are present. In this paper we propose Bayes estimation of the next failure time after the system has experienced some failures, that is, Mean Time Between Failure for the MPLP model. Numerical examples illustrate the estimation procedure.

• Journal of the Korean Statistical Society
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• v.31 no.1
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• pp.17-31
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• 2002

This paper considers noninformative priors for the power law process under failure truncation. Jeffreys'priors as well as reference priors are found when one or both parameters are of interest. These priors are compared in the light of how accurately the coverage probabilities of Bayesian credible intervals match the corresponding frequentist coverage probabilities. It is found that the reference priors have a definite edge over Jeffreys'prior in this respect.

• Journal of the Korean Statistical Society
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• v.34 no.4
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• pp.331-344
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• 2005

Inference on current data could be more reliable if there exist similar data based on previous studies. Ibrahim and Chen (2000) utilize these data to characterize the power prior. The power prior is constructed by raising the likelihood function of the historical data to the power $a_o$, where $0\;{\le}\;a_o\;{\le}\;1$. The power prior is a useful informative prior in Bayesian inference. However, for model selection or model comparison problems, the propriety of the power prior is one of the critical issues. In this paper, we suggest two joint power priors for the power law process and show that they are proper under some conditions. We demonstrate our results with a real dataset and some simulated datasets.

• International Journal of Quality Innovation
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• v.5 no.1
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• pp.1-9
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• 2004

The nonhomogeneous poisson process (NHPP) is often used to model repairable systems that are subject to a minimal repair strategy, with negligible repair times. In this situation, the system can be characterized by its intensity function. There have been many NHPP models according to intensity functions. However, the intensity function of system in use can be changed because of repair or its aging. We consider the single change point model as the modification of the power law process. The shape parameter of its intensity function is changed before and after the change point. We detect the presence of the change point using Bayesian methodology. Some numerical results are also presented.

• Journal of Applied Reliability
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• v.9 no.2
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• pp.121-130
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• 2009

The power law process model the Rate of occurrence of failures(ROCOF) with monotonic trend during the operating time. However, the power law process is inappropriate when a non-monotonic trend in the failure data is observed. In this paper we deals with the reliability modeling of the failure process of large and complex repairable system whose rate of occurrence of failures shows the non-monotonic trend. We suggest a sectional model and a change-point test based on the Schwarz information criterion(SIC) to describe the non-monotonic trend. Maximum likelihood is also suggested to estimate parameters of sectional model. The suggested methods are applied to field data from an repairable system.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.535-541
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• 2007

The Non-homogeneous Poisson process is probably the most popular model since it can model systems that are deteriorating or improving. The renewal process is a model that is often used to describe the random occurrence of events in time. But both these models are based on too restrictive assumptions on the effect of the repair action. The Modulated Power Law Process is a suitable model for describing the failure pattern of repairable systems when both renewal-type behavior and time trend are present. In this paper we propose maximum likelihood estimation of the next failure time after the system has experienced some failures, that is, Mean Time Between Failure for the MPLP model.

• Proceedings of the Korean Reliability Society Conference
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• 2006.05a
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• pp.75-75
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• 2006

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2000.10a
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• pp.66-69
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• 2000

In order to analyze the densification behaviour of stainless steel powder compacts during hot isostatic pressing (HIP) at elevated temperatures, a power-law creep constitutive model based on the plastic deformation theory for porous materials was applied to the densification. Various densification mechanisms including interparticle boundary diffusion, grain boundary diffusion and lattice diffusion mechanisms were incorporated in the constitutive model, as well. The power-law creep model in conjunction with various diffusion models was applied to the HIP process of 316L stainless steel powder compacts under 50 and 100 MPa at 1125 $!`\acute{\dot{E}}$. The results of the calculations were verified using literature data It could be found that the contribution of the diffusional mechanisms is not significant under the current process conditions.

• Journal of Powder Materials
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• v.7 no.3
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• pp.137-142
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• 2000

In order to analyze the densification behaviour of stainless steel powder compacts during hot isostatic pressing (HIP) at elevated temperatures, a power-law creep constitutive model based on the plastic deformation theory for porous materials was applied to the densification. Various densification mechanisms including interparticle boundary diffusion, grain boundary diffusion and lattice diffusion mechanisms were incorporated in the constitutive model, as well. The power-law creep model in conjunction with various diffusion models was applied to the HIP process of 316L stainless steel powder compacts under 50 and 100 MPa at $1125^{\circ}C$. The results of the calculations were verified using literature data. It could be found that the contribution of the diffusional mechanisms is not significant under the current process conditions.

• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.483-496
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• 2005

We consider the power law process which is assumed to have multiple changepoints. We propose a binary segmentation procedure for locating all existing changepoints. We select one model between the no-changepoints model and the single changepoint model by the Bayes factor. We repeat this procedure until no more changepoints are found. Then we carry out a multiple test based on the Bayes factor through the intrinsic priors of Berger and Pericchi (1996) to investigate the system behaviour of failure times. We demonstrate our procedure with a real dataset and some simulated datasets.