• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.41 no.5
• /
• pp.381-389
• /
• 2017

The kernel density was determined based on sampling points obtained in a Markov chain simulation and was assumed to be an important sampling function. A Kriging metamodel was constructed in more detail in the vicinity of a limit state. The failure probability was calculated based on importance sampling, which was performed for the Kriging metamodel. A pre-existing method was modified to obtain more sampling points for a kernel density in the vicinity of a limit state. A stable numerical method was proposed to find a parameter of the kernel density. To assess the completeness of the Kriging metamodel, the possibility of changes in the calculated failure probability due to the uncertainty of the Kriging metamodel was calculated.

• Journal of Korean Society of Environmental Engineers
• /
• v.35 no.11
• /
• pp.763-768
• /
• 2013

The simulation investigation on the availability with failure density function of major equipment for a sewage treatment plant has been carried out. This study focuses on the availability of the plant and criticality with equipment module induced by component layout and its failure function. The equipment classification of sewage treatment plant and its failure function are established. Also solution methodologies are introduced as Monte-Carlo simulation method and event algorithm for uncertainty problem. The availability in the case of serial connection of equipment with all exponential function is calculated as around 50.4%. In other case of parallel combination with back up equipment, the availability showed over 80.1%. The criticality that a ffects availability showed high value over 77% in the dehydration and concentration process of sludge.

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

• Journal of Korean Institute of Industrial Engineers
• /
• v.7 no.1
• /
• pp.13-20
• /
• 1981

Three inspection-ordering policies of a part with three types of lead times, i. e., expedited lead time, special lead time and regular lead time are considered. Policy I : The original part is replaced by a spare immediately after delivery, even if the original part is still operating. Policy II : The delivered spare is put into inventory until the original part failes. Policy III : The original part is inspected once again immediately after the delivery of the spare. If it is in a good state, the original part is used up to its mean degradation time, then replaced. If it is in a degradation state, the original part is replaced by a spare. A cost effectiveness for each policy is analyzed. Optimal inspection-ordering policy which maximizes a cost effectiveness is obtained. Time to degradation distribution and time to failure distribution are assumed to be Weibull and exponential, respectively. Variations of policies are observed with respect to variations of associated costs.

• Journal of the military operations research society of Korea
• /
• v.35 no.2
• /
• pp.33-49
• /
• 2009

The objective of this study is as follows. First, the hazard function on the failure probability density function of the K-1 tracked vehicles can be occurred in the form of the bathtub curve. Second, the failure mode may be different under two different operational situations. The research result shows that the bathtub curve can be fitted in the Weibull distribution, that assumes different shapes according to the specific stage of the system's life cycle. The K-1 tracked vehicle has a relatively high hazard(failure) rate at the time of its first service. The failure rate starts decreasing for a time immediately after it goes into service. After the break-in period, the surviving components have a fairly constant hazard rate. As the K-1 system ages, deterioration of its various parts takes place and the hazard rate starts Increasing. Second, the result shows the failure rate in the harsh operational environment is higher than that in the mild operational environment. In conclusion, the bathtub curve can be logically appropriate in establishing the depot overhaul cycle. Moreover, it is necessary for determining the right time of the depot overhaul to consider not only the age of defense equipment but also the different operational environment.

• Journal of the Korea Safety Management & Science
• /
• v.11 no.1
• /
• pp.87-91
• /
• 2009

본 논문은 상대적으로 새로운 기법인 Parzen Density Estimation과 Multi-class SVM을 이용한 지능형 고장 탐색과 진단 방법을 제안하고 있다. 본 연구에서는 롤링 베어링을 대상으로 고장을 탐색하고 진단하기 위한 방법을 제안하는데 Parzen Density Estimation과 Multi-class SVM은 고장 클래스를 잘 표현할 수 있다. Parzen Density Estimation은 새로운 패턴 데이터의 거절과 알려진 데이터 패턴의 밀도의 평가에 의해 새로운 패턴을 찾아낼 수 있고, Multi-class SVM 기반의 방법은 여러 클래스의 고장을 support vector로 표현하여 고장 패턴을 찾아낼 수 있다. 본 연구에서는 실제의 다중 클래스를 가지는 롤링 베어링의 고장 데이터를 사용하여 고장 패턴을 탐색하는 과정을 보여주는데, 커널함수의 적절한 파라미터의 선택에 의한 Multi-class SVM 기반의 방법이 multi-layer perceptron이나 Parzen Density Estimation 방법보다 우수함을 입증한다.

• Journal of the Korean Society of Propulsion Engineers
• /
• v.16 no.4
• /
• pp.33-41
• /
• 2012

The aim of the study is to investigate the method to estimate a shelf life of KM6 single base propellant by stochastic gamma process model. The state failure level is assumed that the degradation content of stabilizer is below 0.8%. The constant of time dependent shape function and the scale parameter of stationary gamma process are estimated by moment method. The state distribution at each storage time can be shown from probability density function of deterioration. It is estimated that the $B_{10}$ life, a time at which the cumulative failure probability is 10%, is 25 years and the $B_{50}$ life is 36 years from cumulative failure distribution function curve. The $B_{50}$ life can be treated as the average shelf life from the practical viewpoint and the lifetime can be expressed as distribution curve by using stochastic process theory.

• Journal of the Korean Society for Railway
• /
• v.20 no.4
• /
• pp.491-499
• /
• 2017

Railway turnout systems are one of the most important systems in a railway and abnormal turnout systems can cause serious accidents. To detect an abnormal state of a turnout, turnout clearance detectors are widely used. These devices consider a failure of a turnout clearance detectors to be a failure of the turnout system, that could hinder train operations. Analysis of turnout clearance detector failures is very important to ensure normal train operation. We categorized failures of detectors into four groups to identify failure characteristics of the 140 detectors, which are composed of main line detectors (A), side tracks (B), detectors that are in operation more than 80 times a day (C) and detectors that are in operation fewer than 10 times per day. Failures of detectors have mainly been caused in the control part, in the cables and sensors; failures are classified into four groups (A, B, C and D). We have tried to find failure density distributions for each type of failures, inferring the parameter distributions a priori. Finally, using the Bayesian inference we proposed a maintenance time for control parts through the mean time of the detector, life and the life expectancy.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.35 no.10
• /
• pp.1299-1306
• /
• 2011

In this study, crack-growth model parameters subjected to variable amplitude loading are estimated in the form of a probability distribution using the method of Bayesian parameter estimation. Huang's model is employed to describe the retardation and acceleration of the crack growth during the loadings. The Markov Chain Monte Carlo (MCMC) method is used to obtain samples of the parameters following the probability distribution. As the conventional MCMC method often fails to converge to the equilibrium distribution because of the increased complexity of the model under variable amplitude loading, an improved MCMC method is introduced to overcome this shortcoming, in which a marginal (PDF) is employed as a proposal density function. The model parameters are estimated on the basis of the data from several test specimens subjected to constant amplitude loading. The prediction is then made under variable amplitude loading for the same specimen, and validated by the ground-truth data using the estimated parameters.