• Proceedings of the Korean Information Science Society Conference
• /
• 2002.04b
• /
• pp.286-288
• /
• 2002

본 연구에서는 자동화된 부품생산 공장에서의 실시간 통계적 공정관리 시스템의 구축사례를 소개하고, 생산현장에서 지속적으로 올라오는 많은 양의 품질정보를 실시간으로 처리하기 위해 적용된 우선순위를 고려한 분산처리방식을 응용한 알고리즘을 제시한다.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1995.04a
• /
• pp.173-181
• /
• 1995

통계적 공정관리 분야가 점차 컴퓨터를 이용한 관리를 하게 되므로써 공정관리의 온라인화, 실시간화가 되고 있는 가운데, 기존의 관리도는 공정의 파라미터를 기지의 상수값으로 간주하므로 모형이 잘못 설정되었을 경우 상당히 많은 오보(false alarm)를 발생하게 된다. 이에 본 연구는 연속생산공정에서, 칼만필터를 적용하여 공정의 불확실한 파라미터와 모형의 오차에도 불구하고 공정의 변화를 보다 빠르고 정확하게 탐지할 수 있는 관리도를 설계하였다. 본 연구에서 설계한 관리도는 관축치들간에 시간적 종속성이 존재하는 경우에 있어서, 관측치들을 시계열모형으로 묘사를 하여, 파라미터(parameter)를 추정하고, 잔차를 얻어서 만든 잔차관리도로서, 실시간으로 생산공정을 관리하는 경우 효과적임을 보이기 위하여, 컴퓨터 시뮬레이션을 통해 ARL을 구하여 기존에 사용되고 있는 관리도와 수행도를 비교.평가하였다.

• The Korean Journal of Applied Statistics
• /
• v.9 no.1
• /
• pp.91-109
• /
• 1996

Exponentially weighted moving averae(EWMA) control chart has been used widely for process monitoring and process adjustment recently, but there has not been many studies about the selection of the parameters. Design of the control chart can be classified into the statistical design and the economic design. The purpose of the economic design is to minimize the cost function in which all the possible costs occurring during the process are probability given the Type I error probability. In this paper the optimal parameters of the EWMA chart are selected for the economic design as well as for the statistical design. The optimal parameters for the economic design show significantly different from those of the statistical design, and especially the weight is always larger than that used in the statistical design. In the economic design, we divide the model into the single assignable cause model and the multiple assignable causes model caacording to number of which is used as the average context of the multiple assignable causes, it shows that the selection of the parameters may be misleading when the multiple assignable causes exist in practice.

• Journal of the Korean Society for Precision Engineering
• /
• v.15 no.2
• /
• pp.75-80
• /
• 1998

Process information is known to have the continuous distribution in many manufacturing processes. Generalized p-chart has been developed for controlling processes by classifying the information characteristics into several groups. But it is improper to describe continuous processes with the classified process informal ion, which is based on the classical set concept. Fuzzy control chart, has been developed for the control of linguistic data, but it is also based on the dichotomous notion of classical set theory. In this paper, fuzzy sampling method is studied in order to process the uncertain data properly. The method is incorporated with the fuzzy control chart. Statistical characteristics of the fuzzy representative value are utilized to device the fuzzy-statistical control chart. The fuzzy-statistical control chart is compared with the generalized p-chart and both the sensitivity to the process information distribution change pared robustiness against the noise on the process information of the fuzzy-statistical control chart are shown to be superior.

• The Korean Journal of Applied Statistics
• /
• v.24 no.4
• /
• pp.657-676
• /
• 2011

Properties of statistical process control procedures may not be derived analytically in many cases; however, the application of a Markov chain can solve such problems. This article shows how to derive the properties of the process control procedures using the generated Markov chains when the control statistic satisfies the Markov property. Markov chain approaches that appear in the literature (such as the statistical design and economic design of the control chart as well as the variable sampling rate design) are reviewed along with the introduction of research results for application to a new control procedure and reset chart. The joint application of a Markov chain approach and analytical solutions (when available) can guarantee the correct derivation of the properties. A Markov chain approach is recommended over simulation studies due to its precise derivation of properties and short calculation times.

• The Korean Journal of Applied Statistics
• /
• v.34 no.4
• /
• pp.537-556
• /
• 2021

Properties of statistical process control procedures may not be derived analytically in many cases; however, the application of a Markov chain can solve such problems. This article shows how to derive the properties of the process control procedures using the generated Markov chains when the control statistic satisfies the Markov property. Markov chain approaches that appear in the literature (such as the statistical design and economic design of the control chart as well as the variable sampling rate design) are reviewed along with the introduction of research results for application to a new control procedure and reset chart. The joint application of a Markov chain approach and analytical solutions (when available) can guarantee the correct derivation of the properties. A Markov chain approach is recommended over simulation studies due to its precise derivation of properties and short calculation times.

• Journal of the Korean Data and Information Science Society
• /
• v.22 no.4
• /
• pp.679-690
• /
• 2011

Two widely used approaches for improving the quality of the output of a process are statistical process control (SPC) and automatic process control (APC). In recent hybrid processes that combine aspects of the process and parts industries, process variations due to both the inherent wandering and special causes occur commonly, and thus simultaneous application of APC and SPC schemes is needed to effectively keep such processes close to target. The simultaneous implementation of APC and SPC schemes is called integrated process control (IPC). In the IPC procedure, the output variables are monitored during the process where adjustments are repeatedly done by its controller. For monitoring the APC-controlled process, control charts can be generally applied to the output variable. However, as an alternative, some authors suggested that monitoring the input variable may improve the chance of detection. In this paper, we evaluate the performance of several monitoring statistics, such as the output variable, the input variable, and the difference variable, for efficiently monitoring the APC-controlled process when we assume IMA(1,1) noise model with a minimum mean squared error adjustment policy.

• The Korean Journal of Applied Statistics
• /
• v.21 no.2
• /
• pp.247-264
• /
• 2008

This paper considers the integrated process control procedure for detecting special causes in an ARIMA(0,1,1) process that is being adjusted automatically after each observation using a minimum mean squared error adjustment policy. It is assumed that a special cause can change the process mean and the process variance. We derive expressions for the process deviation from target for a variety of different process parameter changes, and introduce a control chart, based on the generalized likelihood ratio, for detecting special causes. We also propose the integrated process control scheme bases on the future loss. The future loss denotes the cost that will be incurred in a process remaining interval from a true out-of-control signal.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1995.10a
• /
• pp.411-414
• /
• 1995

Statistical Process Control (SPC) is a set of methodologies for signaling the presence of undesired sources of variation in manufacturing processes. Expert System in SPC can serve as a valuable tool to automate the analysis and interpretation of control charts. In this paper we put forward a method of successful application of Expert System to SPC in manufacturing process.

• Convergence Security Journal
• /
• v.12 no.6
• /
• pp.85-92
• /
• 2012

There are many software reliability models that are based on the times of occurrences of errors in the debugging of software. It is shown that it is possible to do asymptotic likelihood inference for software reliability models based on infinite failure model and non-homogeneous Poisson Processes (NHPP). For someone making a decision about when to market software, the conditional failure rate is an important variables. The finite failure model are used in a wide variety of practical situations. Their use in characterization problems, detection of outliers, linear estimation, study of system reliability, life-testing, survival analysis, data compression and many other fields can be seen from the many study. Statistical Process Control (SPC) can monitor the forecasting of software failure and there by contribute significantly to the improvement of software reliability. Control charts are widely used for software process control in the software industry. In this paper, we proposed a control mechanism based on NHPP using mean value function of log Poission, log-linear and Parto distribution.