• Journal of Korean Society for Quality Management
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• v.25 no.3
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• pp.96-107
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• 1997

The recent stream to reliability prediction is that it is totally inclusive in depth to consider even the operating and environmental condition at the level of finished goods as well as component itselves. In this study, firstly we present the reliability prediction methods by entire failure rate model which failure rate at the system level is added to the failure rate model at the component level. Secondly we build up the improved bases of reliability demonstration through a, pp.ication of Kaplan-Meier, Cumulative hazard, Johnson's methods as non-parametric and Maximum Likelihood Estimator under exponential & Weibull distribution as parametric. And also present the methods of curve fitting to piecewise failure rate under Weibull distribution, PRST (Probability Ratio Sequential Test), curve fitting to S-shaped reliability growth curve, computer programs of each methods. Lastly we show the practical for determination of optimal burn-in time as a method of reliability enhancement, and also verify the practical usefulness of the above study through the a, pp.ication of failure and test data during 1 year.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.6
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• pp.1346-1352
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• 2009

Warranty cost of automobile parts varies depending on the parts failure rate in a warranty region of individual markets. Parts failure rate is significantly affected by usage-rate given that other stressors of individual markets are similar. Accordingly, warranty cost can be predicted by failure modeling which reflects usage-rate and using a stochastic process. In this paper, one-dimensional approach is used by applying accelerated failure time model on the assumption that the usage-rate is linear. Such model can explain changes in parts failure rate depending on the changes in usage-rate since it can be expressed as a function of usage-rate. Therefore, acquisition of usage-rate in a new market will automatically lead to estimate of failure rate even without warranty data and warranty cost of parts can be predicted through a renewal process in replacement cases. A case study using warranty data of two real markets is presented in the application part of this paper.

• Journal of Chest Surgery
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• v.20 no.4
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• pp.673-682
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• 1987

The principal feature of bioprosthetic valve which remains to be completely defined is long-term durability, especially, with regard to the impact of patient age. This report provides extended data regarding valve durability derived from a data base of 515 patients who received lonescu-Shiley bioprosthetic valve between 1978 and 1985; cumulative duration of follow-up was 1562.3 patient-years, with a maximum follow-up duration of 8.7 years. The results of this survey showed as follows: 1] The actuarial freedom from valve failure at 6 years were 43*7% for 0-19 year-old group, 70*1% for 20-39 year-old group and 75*1% for over 40 year-old group separately. 2] Of the causes of valve failure, only the primary tissue failure was markedly influenced by patient`s age [p<0.001], but the prosthetic valve endocarditis was not [p>0.1]. 3] The linealized incidences of primary tissue failure were 7.31% per patient-year in 0-19 year-old group and 0.12% in 20-39 year-old group. 4] The primary tissue failure rate in 0-19 year-old group was 6.36% during first 4 years, but then increased upto 10.95% at postoperative 5 year. Thus we find that in bioprosthetic valve the primary tissue failure is apt to occur when patient is young [especially less than 20 years old] and the postreplacement time passes [especially over 5 years]. The rate of bioprosthesis failure among children and adolescents is clearly higher than that observed in adult patients; however, conclusive quantification of time-related risk for young patient is not yet possible on the basis of existing data. Therefore, the advisability of bioprosthesis implantation in children remains to be determined.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.285-288
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• 2009

The emergency recovery scheme in time of a disaster or accident up to now has been focused on how to build a backup for the public service or communication network in national scale, also, all the mobile operators have done as recovery scheme for the equipment failure in their network was to make a backup copy of billing data or package data, and they have usually studied only on the backup and failure recovery scheme in the circuit-switched network. However, since it has become more probable recently that a disaster or accident could occur in the switching office buildings, a recovery scheme in time of the failure of these entire switching offices became necessary. The purpose of this paper is to propose various recovery schemes, and by having checkups on many issues that could arise while carrying out these schemes, it presents the best solution for each issue. Moreover, it presents the future tasks that need to be studied as well as initial study directions.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.7 no.1
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• pp.62-66
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• 2008

Machine parts subjected to fluctuating or cyclic loads induce repeated stresses that often result in failure by fatigue. In such cases, the fatigue failures unfortunately sometimes occur. These may arise from a lack of knowledge with regard to the design, fabrication and operation of the machines or structures. The failure analysis provides valuable information regarding the prevention of similar failures. Furthermore, this information will be useful to improve or to develop new products. Failure stress analysis is classified into X-ray fractography. X-ray fractography has the limited applications because of material crystal size, difficult measurement method, electrolytic polishing precision, and long test time. Therefore, this study proposed the new method to improve the measurement precision of plastic zone depth and test time using TRIZ.

• International Journal of Reliability and Applications
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• v.4 no.2
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• pp.79-95
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• 2003

Manufacturers collect and analyze field reliability data to enhance the quality and reliability of their products and to improve customer satisfaction. To reduce the data collecting and maintenance costs, the amount of data maintained for evaluating product quality and reliability should be minimized. With this in mind, some industrial companies assemble warranty databases by gathering data from different sources for a particular time period. This “marginal count failure data” does not provide (i) the number of failures by when the product entered service, (ii) the number of failures by product age, or (iii) information about the effects of the operating season or environment. This article describes a method for estimating age-based claim rates from marginal count failure data. It uses covariates to identify variations in claims relative to variables such as manufacturing characteristics, time of manufacture, operating season or environment. A Poisson model is presented, and the method is illustrated using warranty claims data for two electrical products.

• Journal of Korean Society for Quality Management
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• v.32 no.4
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• pp.242-251
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• 2004

Several statistical methods are introduced 1=o analyze the accelerated failure time data. Most frequently used method is the log-linear approach with parametric assumption. Since the accelerated failure time experiments are exposed to many environmental restrictions, parametric log-linear relationship might not be working properly to analyze the resulting data. The models proposed by Buckley and James(1979) and Stute(1993) could be useful in the situation where parametric log-linear method could not be applicable. Those methods are introduced in accelerated experimental situation under the thermal acceleration and discussed through an illustrated example.

• Communications for Statistical Applications and Methods
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• v.19 no.2
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• pp.219-224
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• 2012

This article considers the mean time to failure(MTTF) of a dependent parallel system. We study how the degree of dependency components influences the increase in the mean lifetime for this system. The results are illustrated by tables and figures.

• Journal of the Korea Society for Simulation
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• v.3 no.1
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• pp.89-98
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• 1994

In this paper, a criteria for selecting an appropriate load redistribution algorithm is devised so that a fault-tolerance distributed system can operte at its optimal efficience. To present the guideline for selecting redistributing algorithms, simulation models of fault-tolerant system including redistribution algorithms are developed using SLAM II. The job arrival rate, service rate, failure and repair rate of nodes, and communication delay time due to load migration are used as parameters of simulation. The result of simulation shows that the job arrival rate and the failure rate of nodes are not deciding factors in affecting the relative efficiency of algorithms. Algorithm B shows relatively a consistent performance under various environments, although its performance is between those of other algorithms. If the communication delay time is longer than average job processing time, the performance of algorithm B is better than others. If the repair rate is relatively small or communication delay time is longer than service time, algorithm A leads to good performance. But in opposite environments, algorithm C is superior to other algorithms.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.1
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• pp.123-131
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• 2020

There have been a lot of studies in the past for the method of predicting the failure of a machine, and recently, a lot of researches and applications have been generated to diagnose the physical condition of the machine and the parts and to calculate the remaining life through various methods. Survival models are also used to predict plant failures based on past anomaly cycles. In particular, special machine that reflect the fluid flow and process characteristics of chemical plants are connected to hundreds or thousands of sensors, so there are not many factors that need to be considered, such as process and material data as well as application of derivative variables. In this paper, the data were preprocessed through time series anomaly detection based on unsupervised learning to predict the abnormalities of these special machine. Next, clustering results reflecting clustering-based data characteristics were applied to produce additional variables, and a learning data set was created based on the history of past facility abnormalities. Finally, the prediction methodology based on the supervised learning algorithm was applied, and the model update was confirmed to improve the accuracy of the prediction of facility failure. Through this, it is expected to improve the efficiency of facility operation by flexibly replacing the maintenance time and parts supply and demand by predicting abnormalities of machine and extracting key factors.