• Journal of Korean Society for Quality Management
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• v.25 no.3
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• pp.86-95
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• 1997

This paper considers a repairable system, which is maintained preventively at periodic times and is minimally repaired at each failure. Most preventive maintenance policies for such repairable systems assume that the cost of minimal repair is constant regardless of its age at failure. However, it is more practical to consider the situations where the cost of minimal repair is dependent not only on its age at failue, but also on the number of preventive maintenance carried out prior to its failure. We consider the preventive maintenance carried out prior to its failure. We consider the preventive maintenance policy with age-dependent minimal repair cost. The optimal policies which minimize the expected cost rate over an infinite time span are discussed. We obtain the optimal period and number of preventive maintenance prior to replacement of the system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.3
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• pp.575-580
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• 2010

This paper is to design an optimum power conversion device for the system required for development of a low cost 3-phase power inverter. For this purpose, in order to meet with endurance required by the industry, failure mode is standardized to guarantee lifetime of a power semiconductor by monitoring real time power loss and to facilitate failure mode analysis. As normality of heat loss of a power semiconductor is identified remaining in a certain range by comparing heat rise slope between that is calculated by using average current or average loss and that is measured at a heat sink, its feasibility is confirmed by experiment.

• International Journal of Reliability and Applications
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• v.7 no.1
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• pp.41-53
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• 2006

The exact cause of the system's failure is often unknown in the masked system lifetime data. In such type of data, there are two observable quantities, namely (i) the systems time to failure and (ii) the set of systems components that contains the component, which might cause the system to fail. Our objective in this paper is to use the maximum likelihood procedure in the presence of masked data to make inference for the reliability of the system's components. We assume a multi-component series system where each component has a constant failure rate. Different cases that permit for closed form solutions of point estimates are considered. The results obtained in this paper generalize other published results.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.17 no.1
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• pp.24-28
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• 2009

The reliability assessment is performed for Pressure Control Regulator of Aircraft Fuel System using reliability procedure which consists of the reliability analysis and the Failure Modes and Effects Analysis(FMEA). The target reliability as MTBF(Mean Time Between Failure) is set to 5000hr. During the reliability analysis process, the system is categorized by Work Breakdown Structure(WBS) up to level 3, and a reliability structure is defined by schematics of the system. Since the components and parts that have been collected through EPRD/NPRD. The predicted reliability to meet mission requirements and operating conditions is estimated as 4375.9hr. To accomplish the target reliability, the components and parts with high RPN have been identified and changed by analyzing the potential failure modes and effects. By changing the configuration design of components and parts with high-risk, the design is satisfied target reliability.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2005.10a
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• pp.135-138
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• 2005

This study began to establish a risk evaluation method for irrigation reservoirs under the overtopping failure mode. To define the risk, reliability analysis was performed using time series of reservoir flood inflow and spillway outflow. The former was defined as a load and the latter was the resistance component. The method results in failure probability, which is calculated by convolution multiplication between probability distribution functions of both components. The proposed method was applied to 3 reservoir sites and each failure probability was determined as 0.0012, 0.00001, and 0.000001 respectively.

• Journal of the Korean Society of Safety
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• v.21 no.1 s.73
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• pp.28-34
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• 2006

A procedure of estimating failure probability is demonstrated for a pressurized pipe of CrMo steel used at $538^{\circ}C$. Probabilistic fracture mechanics were employed considering variations of pressure loading, material properties and geometry. Probability density functions of major material variables were determined by statistical analyses of implemented data obtained by previous experiments. Distributions of the major variables were reflected in Monte Carlo simulation and failure probability as a function of operating time was determined. The creep crack growth life assessed by conventional deterministic approach was shown to be conservative compared with those obtained by probabilistic one. Sensitivity analysis for each input variable was also conducted to understand the most influencing variables to the residual life analysis. Internal pressure, creep crack growth coefficient and creep coefficient were more sensitive to failure probability than other variables.

• Journal of Applied Reliability
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• v.17 no.4
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• pp.343-354
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• 2017

Purpose: This paper evaluates the reliability of tapered grid couplings and presents test results through performance and life tests. Methods: The performance and life test method were presented by analyzing the failure modes for the tapered grid coupling. Zero failure test time was calculated to evaluate the reliability of tapered grid couplings and the test was performed under accelerated conditions. The nondestructive test and wear analysis using weighing were also carried out to check the failure modes of the field conditions. Conclusion: This study can be provided to improve the product reliability through failure analysis of a tapered grid coupling. The performance test results of before and after the accelerated life test were presented to confirm the improved reliability of the tapered grid coupling.

• Journal of Yeungnam Medical Science
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• v.7 no.1
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• pp.81-93
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• 1990

The author fractographically analyized the cause of metal failure(the first time this procedure has been used for this metal failure)and also analyized it clinically. In this study, I selected eight cases which have been analyized fractographically. In all these cases, the analysis was done after treatment of metal failure of implants internally fixed to femur shaft fractures at the Department of Orthopedic Surgery, Yeung-Nam University Hospital during the six year period from May 1983 to September 1989. 1. Metal failure occured in five dynamic-compression plates, one Jewett nail, one screw in Rowe plate, and one interlocking nail. 2. The clinical cause of metal failure was deficiency of medial butress in five cases, incorrect position of implant in one case, and incorrect selection of implant in two cases. 3. The time interval between internal fixation and metal failure was four months in one case, between five months to twelve months in six cases, three years in one case. 4. The fractographically analytical cause of metal failure was ; first, impact failure, one case, second, fatigue failure, six cases, machining mark(stress liser), four cases type : low consistent cyclic fatigue failure irregular cyclic fatigue failure third, stress corrosion crack, one case. 5. 316L Stainless Steel has good resistance to corrosion. However, when its peculiar surface film is destroyed by fretting, it shows pitting corrosion. This is, perhaps, the main cause of metal failure. 6. It is possible that mechanical injury occured in implants during the manufacturing of implants or that making a screw hole is the main cause of metal failure.

• Journal of Applied Reliability
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• v.6 no.3
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• pp.195-203
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• 2006

This paper deals with cost analysis model in periodic maintenance policy. The repair policy with minimal repair is considered as follow : as the occurrence of failure between minimal repair and periodic interval time, unit is replaced by a new unit before the periodic maintenance time comes. Then total expected cost per unit time is calculated according to time delta t in a view of customer's. The total expected costs are included repair and usage cost : operating, fixed, minimal repair, periodic maintenance and new unit expected cost. Numerical example is shown in which failure time of item has Normal distribution.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2016-2029
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• 2023

Accurate prediction of critical illness is significant for ensuring the lives and health of patients. The selection of indicators affects the real-time capability and accuracy of the prediction for critical illness. However, the diversity and complexity of these indicators make it difficult to find potential connections between them and critical illnesses. For the first time, this study proposes an indicator analysis model to extract key indicators from the preoperative and intraoperative clinical indicators and laboratory results of critical illnesses. In this study, preoperative and intraoperative data of heart failure and respiratory failure are used to verify the model. The proposed model processes the datum and extracts key indicators through four parts. To test the effectiveness of the proposed model, the key indicators are used to predict the two critical illnesses. The classifiers used in the prediction are light gradient boosting machine (LightGBM) and eXtreme Gradient Boosting (XGBoost). The predictive performance using key indicators is better than that using all indicators. In the prediction of heart failure, LightGBM and XGBoost have sensitivities of 0.889 and 0.892, and specificities of 0.939 and 0.937, respectively. For respiratory failure, LightGBM and XGBoost have sensitivities of 0.709 and 0.689, and specificity of 0.936 and 0.940, respectively. The proposed model can effectively analyze the correlation between indicators and postoperative critical illness. The analytical results make it possible to find the key indicators for postoperative critical illnesses. This model is meaningful to assist doctors in extracting key indicators in time and improving the reliability and efficiency of prediction.