• Journal of the Korea Safety Management & Science
• /
• v.12 no.4
• /
• pp.255-263
• /
• 2010

The research developes short-run standardized control charts(SSCC) and short-run acceptance control charts(SACC) under the various demand patterns. The demand patterns considered in this paper are three types such as high-variety and repetitive low-volume pattern, extremely-high-variety and nonrepetitive low-volume pattern, and high-variety and extremely-low-volume pattern. The short-run standardized control charts developed by extending the long-run ${\bar{x}}$-R, ${\bar{x}}$-s and I-MR charts have strengths for practioners to understand and use easily. Moreover, the short-range acceptance control charts developed in the study can be efficiently used through combining the functions of the inspection and control chart. The weighting schemes such as Shewhart, moving average (MA) and exponentially weighted moving average (EWMA) can be considered by the reliability of data sets. The two types according to the use of control chart are presented in the short-range standardized charts and acceptance control charts. Finally, process capability index(PCI) and process performance index(PPI) classified by the demand patterns are presented.

• Journal of Korean Institute of Industrial Engineers
• /
• v.31 no.1
• /
• pp.87-98
• /
• 2005

This research investigates economic-statistical characteristics of variable sampling size and interval (VSSI)$\bar{X}$charts under two assignable causes. A Markov chain approach is employed in order to calculate average run length (ARL) and average time to signal (ATS). Six transient states are derived by carefully defining the state. A steady state cost rate function is constructed based on Lorenzen and Vance(1986) model. The cost rate function is optimized with respect to six design parameters for designing the VSSI $\bar{X}$ charts. Computational experiments show that the VSSI $\bar{X}$ chart is superior to the Shewhart $\bar{X}$ chart in the economic-statistical sense, even under two assignable causes. A comparative study shows that the cost rate may increase up to almost 30% by overlooking the second cause. Critical input parameters are also derived from a sensitivity study and a few guideline graphs are provided for determining the design parameters.

• Journal of Korean Society for Quality Management
• /
• v.28 no.2
• /
• pp.39-56
• /
• 2000

${\overline{X}}$ control chart has proven to be an effective tool to improve the product quality. Shewhart charts assume that the observations are independent and normally distributed. Under the presence of positive autocorrelation and severe skewness, the control limits are not accurate because assumptions are violated- Autocorrelation in process measurements results in frequent false alarms when standard control chats are applied in process monitoring. In this paper, Threshold Bootstrap and Moving Block Bootstrap are used for constructing a confidence interval of correlated observations. Monte Carlo simulation studies are conducted to compare the performance of the bootstrap methods and that of standard method for constructing control charts under several conditions.

• Proceedings of the Safety Management and Science Conference
• /
• 2013.04a
• /
• pp.533-546
• /
• 2013

이 논문은 미세공정변동에서 극소불량을 감지하는 관리도의 경제적 설계를 개발하기 위한 조사연구이다. 일반적인 관리도의 설계는 통계적 설계와 경제적 설계로 구분할 수 있다. 공정의 변동 원인에 따라 샘플의 간격(h), 샘플의 크기(n), 관리한계선(k) 등의 설계 모수를 최적접근방법으로 결정을 하는 경제적 설계의 모델을 조사하였다. 관리도의 경제적 설계는 공정의 관리이상상태를 효율적으로 감지하여 관리상태로 정상화 시키는 것에 대한 공정의 개선비용과 기대품질비용을 절약 할 수 있는 최적설계 방안이다. 그리고 Shewhart 관리도의 X-bar 통계량으로 극소불량을 검출 하는것에 한계가 있기 때문에 Zp 통계량과 분포를 설계하여 극소불량을 빠르게 감지할 수 있는 Zp 관리도의 설계를 적용하고, 미세공정변동을 정확하게 감지할 수 있는 CUSUM 관리도를 동시에 적용하였다. 따라서, 미세공정변동과 극소불량을 동시에 관리 할 수 있는 Zp-CUSUM 관리도의 통계적 설계 구조를 체계화 하였으며, 기존의 경제적 설계의 모델을 비교 분석하여 새로운 경제적 설계에 대한 모델을 제안하고자 한다.

• Journal of the Korean Data and Information Science Society
• /
• v.27 no.6
• /
• pp.1487-1498
• /
• 2016

In monitoring a process, in-control process parameters must be estimated from the Phase I data. When we design the control chart based on the estimated process parameters, the control limits are usually chosen to satisfy a specific in-control average run length (ARL). However, as the run length distribution is skewed when the process is either in-control or out-of-control, the median run length (MRL) can be used as alternative measure instead of the ARL. In this paper, we evaluate the performance of Shewhart $\bar{X}$ chart with estimated parameters in terms of the average of median run length (AMRL) and the standard deviation of MRL (SDMRL) metrics. In simualtion study, the grand sample mean is used as a process mean estimator, and several competing process standard deviation estimators are used to evaluate the in-control performance for various amounts of Phase I data.

• Proceedings of the KIEE Conference
• /
• 2006.07a
• /
• pp.22-24
• /
• 2006

The installations of power quality monitoring system have increased drastically over the past several decades. These systems have been effectively used to monitor, analyze and diagnose the conditions of power system, and furthermore can be used to improve the present asset maintenance policy, scheduled (time-based) method, into the advanced, cost-effective and labor-effective maintenance methods, such as condition-based maintenance, predictive maintenance and reliability centered maintenance. As an approach to this, this paper introduces the statistical methods, three kinds of control charts (Shewhart chart, CUSUM chart and EWMA chart), and discusses the applicability of these methods to recognize the changing trends of power quality indices and to estimate the system's condition, using Matlab.

• Proceedings of the Korean Society for Quality Management Conference
• /
• 2007.04a
• /
• pp.302-309
• /
• 2007

To conduct statistical process control needs the assumption that the process data are independent. However, most of chemical processes, like a semi-conduct processes do not satisfy the assumption because of autocorrelation. It causes abnormal out of control signal in the process control and misleading process capability. In this study, we introduce that Shore's method to solve the problem and to find the optimal subgroup size to estimate variance for AR(l) model. Especially, we focus on finding an actual subgroup size for small samples using simulation. It may be very useful for statistical process control to analyze process capability and to make a Shewhart chart properly.

• Journal of the Korean Data and Information Science Society
• /
• v.22 no.4
• /
• pp.803-809
• /
• 2011

Properties of multivariate Shewhart and CUSUM charts for monitoring variance-covariance matrix, specially focused on correlation coefficient components, are investigated. The performances of the proposed charts based on control statistic Lawley-Hotelling $V_i$ and likelihood ratio test (LRT) statistic $TV_i$ are evaluated in terms of average run length (ARL). For monitoring correlation coe cient components of dispersion matrix, we found that CUSUM chart based on $TV_i$ gives relatively better performances and is more preferable, and the charts based on $V_i$ perform badly and are not recommended.

• Structural Engineering and Mechanics
• /
• v.81 no.5
• /
• pp.647-664
• /
• 2022

The powerful data mapping capability of computational deep learning methods has been recently explored in academic works to develop strategies for structural health monitoring through appropriate characterization of dynamic responses. In many cases, these studies concern laboratory prototypes and finite element models to validate the proposed methodologies. Therefore, the present work aims to investigate the capability of a deep learning algorithm called Sparse Autoencoder (SAE) specifically focused on detecting structural alterations in real-case studies. The idea is to characterize the dynamic responses via SAE models and, subsequently, to detect the onset of abnormal behavior through the Shewhart T control chart, calculated with SAE extracted features. The anomaly detection approach is exemplified using data from the Z24 bridge, a classical benchmark, and data from the continuous monitoring of the San Vittore bell-tower, Italy. In both cases, the influence of temperature is also evaluated. The proposed approach achieved good performance, detecting structural changes even under temperature variations.

• Journal of Korean Society for Quality Management
• /
• v.27 no.4
• /
• pp.143-152
• /
• 1999

In Shewhart control chart, the average run length(ARL) is calculated using the mean of a conventional geometric distribution(CGD) assuming a sequence of identical and independent Bernoulli trials. In this, the success probability of CGB is the probability that any point exceeds the control limits. When the process is in-control state, there is no problem in the above assumption since the probability that any point exceeds the control limits does not change if the in-control state continues. However, if the out-of-control state begins and continues during the process, the probability of exceeding the control limits may take two forms. First, once the out-of-control state begins with exceeding probability p, it continues with the same exceeding probability p. Second, after the out-of-control state begins, the exceeding probabilities may very according to some pattern. In the first case, ARL is the mean of CGD with success probability p as usual. But in the second case, the assumption of a sequence of identical and independent Bernoulli trials is invalid and we can not use the mean of CGD as ARL. This paper concentrate on that point. By adopting one generalized binomial distribution(GBD) model that allows correlated Bernoulli trials, generalized geometric distribution(GGD) is defined and its mean is derived to find an alternative ARL when the process is in out-of-control state and the exceeding probabilities take the second form mentioned in the above. Small-scale simulation is performed to show how an alternative ARL works.