• International Journal of Reliability and Applications
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• v.13 no.2
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• pp.81-90
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• 2012

Engineering systems are usually repairable. The reliability of a repairable system can be represented by failure intensity function. A type of shape of failure intensity function is called a failure pattern. Reliability-Centred Maintenance (RCM) presents six typical failure patterns but its definition is unclear. It is an open issue how to recognize the failure pattern of repairable systems. This paper first discusses the problems of RCM with the notion of failure pattern; then presents the method for failure pattern recognition; and finally proposes a flexible failure intensity function model. The appropriateness of the model is illustrated by a real-world example.

• International Journal of Reliability and Applications
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• v.2 no.3
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• pp.199-207
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• 2001

The failure rate functions between successive failures are of concatenated form. We allow the parameters of failure rate function change after a certain failure and its fixing. We confine out attention to a model wherein the interfailure times are described by its failure rate function. We suggest an adaptive failure rate function with a change-point under the assumption that interfailure times are record value statistics from a Weibull distribution. The proposed model will be applied through a practical example of software failure data.

• Journal of Applied Reliability
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• v.16 no.1
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• pp.56-63
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• 2016

Purpose: The purpose of this study is to point out that the Kaplan-Meier method is not valid to calculate the survival probability or failure probability (risk) in the presence of competing risks and to introduce more valid method of cumulative incidence function. Methods: Survival analysis methods have been widely used in biostatistics division. However the same methods have not been utilized in reliability division. Especially competing risks cases, where several causes of failure occur and the occurrence of one event precludes the occurrence of the other events, are scattered in reliability field. But they are not noticed in the realm of reliability expertism or they are analysed in the wrong way. Specifically Kaplan-Meier method which assumes that the censoring times and failure times are independent is used to calculate the probability of failure in the presence of competing risks, thereby overestimating the real probability of failure. Hence, cumulative incidence function is introduced and sample competing risks data are analysed using cumulative incidence function and some graphs. Finally comparison of cumulative incidence functions and regression type analysis are mentioned briefly. Results: Cumulative incidence function is used to calculate the survival probability or failure probability (risk) in the presence of competing risks and some useful graphs depicting the failure trend over the lifetime are introduced. Conclusion: This paper shows that Kaplan-Meier method is not appropriate for the evaluation of survival or failure over the course of lifetime. In stead, cumulative incidence function is shown to be useful. Some graphs using the cumulative incidence functions are also shown to be informative.

• International Journal of Quality Innovation
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• v.5 no.1
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• pp.110-121
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• 2004

There is a new trend of incorporating software coverage metrics into software reliability modelling. This paper proposes a coverage-based software reliability growth model. Firstly, the failure rate function in coverage is analytically derived. Then it is shown that the number of detected faults follows a Nonhomogeneous Poisson distribution of which intensity function is the failure rate function in coverage. Practical applicability of the proposed models is examined by illustrative numerical examples.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.44
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• pp.263-271
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• 1997

This paper presents a preventive maintenance model for determining the preventive replacement period of a system in which a failure rate is affected by the cumulative damage of fault and inspection. Especially, the failure rate function is considered to be a function of the cumulative damage of the fault and inspection time. Types of replacement considered are preventive replacement and failure replacement. Failure rate and expected cost function between replacement are derived. An optimal policy is obtained that minimizes the average cost per unit time for preventive replacement, failure replacement, inspection and repair.

• Clinical and Experimental Pediatrics
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• v.56 no.3
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• pp.101-106
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• 2013

Despite developments in surgical techniques and other interventions, right ventricular (RV) failure remains an important clinical problem in several congenital heart diseases (CHD). RV function is one of the most important predictors of mortality and morbidity in patients with CHD. RV failure is a progressive disorder that begins with myocardial injury or stress, neurohormonal activation, cytokine activation, altered gene expression, and ventricular remodeling. Pressure-overload RV failure caused by RV outflow tract obstruction after total correction of tetralogy of Fallot, pulmonary stenosis, atrial switch operation for transposition of the great arteries, congenitally corrected transposition of the great arteries, and systemic RV failure after the Fontan operation. Volume-overload RV failure may be caused by atrial septal defect, pulmonary regurgitation, or tricuspid regurgitation. Although the measurement of RV function is difficult because of many reasons, the right ventricle can be evaluated using both imaging and functional modalities. In clinical practice, echocardiography is the primary mode for the evaluation of RV structure and function. Cardiac magnetic resonance imaging is increasingly used for evaluating RV structure and function. A comprehensive evaluation of RV function may lead to early and optimal management of RV failure in patients with CHD.

• The KSFM Journal of Fluid Machinery
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• v.19 no.3
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• pp.33-36
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• 2016

The investigation on the verification of availability simulation for small-scale plant has been carried out. This study focuses on the availability variation induced by number of equipment and iteration with failure density function. The equipment classification of small-scale plant and failure type and the methodologies on Monte-Carlo simulation are established. The availability deviation with programs showed under Max. 1.7% for the case of normal function. This method could be used to availability evaluation of small-scale plant, but calibration of the failure density function is necessary for general application.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.310-315
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• 2003

This paper presents the effect of boundary condition of failure pressure model for buried pipelines on failure prediction by using a failure probability model. The first order Taylor series expansion of the limit state function is used in order to estimate the probability of failure associated with various corrosion defects for long exposure periods in years. A failure pressure model based on a failure function composed of failure pressure and operation pressure is adopted for the assessment of pipeline failure. The effects of random variables such as defect depth, pipe diameter, defect length, fluid pressure, corrosion rate, material yield stress, material ultimate tensile strength and pipe thickness on the failure probability of the buried pipelines are systematically studied by using a failure probability model for the corrosion pipeline.

• Journal of Korean Society for Quality Management
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• v.25 no.3
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• pp.51-61
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• 1997

We introduce a method of estimating an unknown failure rate function based on sample data. We estimate failure rate function by a function s from a space of cubic splines constrained to be linear (or constant) in tails using maximum likelihood estimation. The number of knots are determined by Bayesian Information Criterion(BIC). Examples using simulated data are used to illustrate the performance of this method.

• International Journal of Reliability and Applications
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• v.8 no.1
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• pp.83-94
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• 2007

In this paper, we consider a standby redundant structure with a function of switchover processing which may not be not perfect. The switchover processing is governed by a control module whose failure may cause the failure of the whole system. The parameters measuring such an effect of failure of the control module is included in our reliability model. We compute several reliability measures such as reliability function, failure rate, MTBF, mean residual life function, and the steady state availability. We also compare a single unit structure and the redundant structure with regard to those reliability measures. An example is given to illustrate our results.