• 품질경영학회지
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• 제23권3호
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• pp.93-101
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• 1995

We present a brief survey concerning the relations between mean residual life and failure rate. Change points of mean residual life and failure rate are known to be different in general and we explore such situations in this paper. A few parametric models which show bathtub-shaped failure rate are examined in details, including the shape of its corresponding mean residual life function. We give some graphical comparisons of trend changes of mean residual life and failure rate for various choices of parameters for each parametric model.

• 대한산업공학회지
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• 제21권4호
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• pp.609-628
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• 1995

This paper presents a reliability model and a data-analytic procedure for a repairable unit subject to failures due to multiple non-identifiable causes. We regard a failure cause as a state and assume the life distribution for each cause to be exponential. Then we represent the dependency among the causes by a Markov switching model(MSM) and estimate the transition probabilities and failure rates by maximum likelihood(ML) method. The failure data are incomplete due to masked causes of failures. We propose a specific version of EM(expectation and maximization) algorithm for finding maximum likelihood estimator(MLE) under this situation. We also develop statistical procedures for determining the number of significant states and for testing independency between state transitions. Our model requires only the successive failure times of a unit to perform the statistical analysis. It works well even when the causes of failures are fully masked, which overcomes the major deficiency of competing risk models. It does not require the assumption of stationarity or independency which is essential in mixture models. The stationary probabilities of states can be easily calculated from the transition probabilities estimated in our model, so it covers mixture models in general. The results of simulations show the consistency of estimation and accuracy gradually increasing according to the difference of failure rates and the frequency of transitions among the states.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2001년도 춘계학술대회 논문집
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• pp.324-327
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• 2001

This paper shows an example of the Design Review and Common-Cause Failure (CCF) Modeling of mechanical Parts. Reliability should be continuously monitored during the entire period of design. Design Review is the procedure to improve the reliability for the product. We proposed the reliability assessment and design review method. CCF Model is the general dependent model considering the failure mode effects several component simultaneously. This study considers the computation of the network with dependent components. It is important that CCF model is applied for mechanical pars.

• Coupled systems mechanics
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• 제4권4호
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• pp.337-364
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• 2015

In this paper we present methodology for parameters identification of constitutive model which is able to present behavior of a connection between two members in a structure. Such a constitutive model for frame connections can be cast in the most general form of the Timoshenko beam, which can present three failure modes. The first failure mode pertains to the bending in connection, which is defined as coupled plasticity-damage model with nonlinear softening. The second failure mode is seeking to capture the shearing of connection, which is defined as plasticity with linear hardening and nonlinear softening. The third failure mode pertains to the diffuse failure in the members; excluding it leads to linear elastic constitutive law. Theoretical formulation of this Timoshenko beam model and its finite element implementation are presented in the second section. The parameter identification procedure that will allow us to define eighteen unknown parameters is given in Section 3. The proposed methodology splits identification in three phases, with all details presented in Section 4 through three different examples. We also present the real experimental results. The conclusions are stated in the last section of the paper.

• 복합신소재구조학회 논문집
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• 제5권4호
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• pp.1-10
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• 2014

A progressive failure analysis procedure for composite laminates is developed in here and in the companion paper. An anisotropic plastic constitutive model for fiber-reinforced composite material, is developed, which is simple and efficient to be implemented into computer program for a predictive analysis procedure of composites. In current development of the constitutive model, an incremental elastic-plastic constitutive model is adopted to represent progressively the nonlinear material behavior of composite materials until a material failure is predicted. An anisotropic initial yield criterion is established that includes the effects of different yield strengths in each material direction, and between tension and compression. Anisotropic work-hardening model and subsequent yield surface are developed to describe material behavior beyond the initial yield under the general loading condition. The current model is implemented into a computer code, which is Predictive Analysis for Composite Structures (PACS), and is presented in the companion paper. The accuracy and efficiency of the anisotropic plastic constitutive model are verified by solving a number of various fiber-reinforced composite laminates with and without geometric discontinuity. The comparisons of the numerical results to the experimental and other numerical results available in the literature indicate the validity and efficiency of the developed model.

• Nuclear Engineering and Technology
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• 제46권6호
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• pp.773-782
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• 2014

The general path model (GPM) is one approach for performing degradation-based, or Type III, prognostics. The GPM fits a parametric function to the collected observations of a prognostic parameter and extrapolates the fit to a failure threshold. This approach has been successfully applied to a variety of systems when a sufficient number of prognostic parameter observations are available. However, the parametric fit can suffer significantly when few data are available or the data are very noisy. In these instances, it is beneficial to include additional information to influence the fit to conform to a prior belief about the evolution of system degradation. Bayesian statistical approaches have been proposed to include prior information in the form of distributions of expected model parameters. This requires a number of run-to-failure cases with tracked prognostic parameters; these data may not be readily available for many systems. Reliability information and stressor-based (Type I and Type II, respectively) prognostic estimates can provide the necessary prior belief for the GPM. This article presents the Bayesian updating framework to include prior information in the GPM and compares the efficacy of including different information sources on two data sets.

• 품질경영학회지
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• 제30권3호
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• pp.53-65
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• 2002

This paper studies an economic manufacturing quantity (EMQ) model with two types of failures and planned preventive maintenance of the production facility. One is a type I (major) failure which should be corrected by a failure maintenance and the other is a type H (minor) failure which can be minimally repaired without interrupting the production run. The objective is to determine the lot size and preventive replacement policy minimizing the long-run expected cost per unit time. We consider a control policy with a constant production lot size and preventive maintenance after completing n production runs. It is assumed that both preventive and failure maintenance times are random and the demand arriving during a stock-out period is lost. An expression for the expected cost per unit time is obtained in the general case. A special case is discussed and numerical results are provided.

• 한국정보처리학회논문지
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• 제7권7호
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• pp.2076-2085
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• 2000

Almost all existing software reliability models are based on the assumptions of he software usage and software failure process. There, therefore, is no universally applicable software reliability model. To develop a universal software reliability model this paper suggests the predictive filter as a general software reliability prediction model for time domain failure data. Its usefulness is empirically verified by analyzing the failure datasets obtained from 14 different software projects. Based on the average relative prediction error, the suggested predictive filter is compared with other well-known neural network models and statistical software reliability growth models. Experimental results show that the predictive filter generally results in a simple model and adapts well across different software projects.

• 한국산업경영시스템학회:학술대회논문집
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• 한국산업경영시스템학회 2002년도 춘계학술대회
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• pp.139-149
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• 2002

This paper presents an approach to derive probability models for use in structural reliability studies. Two main points are made. First, that it is possible to translate engineering and physics knowledge into a requirement on the form of a probability model. And second, that making assumptions about a probability model for structural failure implies either explicit or hidden assumptions about material and structural properties. The work is foundational in nature, but is developed with explicit examples taken from planar and general stress problems, the von Mises failure criterion, and a modified Weibull distribution.

• 산업경영시스템학회지
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• 제42권4호
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• pp.145-152
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• 2019

All machines deteriorate in performance over time. The phenomenon that causes such performance degradation is called deterioration. Due to the deterioration, the process mean of the machine shifts, process variance increases due to the expansion of separate interval, and the failure rate of the machine increases. The maintenance model is a matter of determining the timing of preventive maintenance that minimizes the total cost per wear between the relation to the increasing production cost and the decreasing maintenance cost. The essential requirement of this model is that the preventive maintenance cost is less than the failure maintenance cost. In the process mean shift model, determining the resetting timing due to increasing production costs is the same as the maintenance model. In determining the timing of machine adjustments, there are two differences between the models. First, the process mean shift model excludes failure from the model. This model is limited to the period during the operation of the machine. Second, in the maintenance model, the production cost is set as a general function of the operating time. But in the process mean shift model, the production cost is set as a probability functions associated with the product. In the production system, the maintenance cost of the equipment and the production cost due to the non-confirming items and the quality loss cost are always occurring simultaneously. So it is reasonable that the failure and process mean shift should be dealt with at the same time in determining the maintenance time. This study proposes a model that integrates both of them. In order to reflect the actual production system more accurately, this integrated model includes the items of process variance function and the loss function according to wear level.