• International Journal of Quality Innovation
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• v.5 no.1
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• pp.52-67
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• 2004

Successful implementation of statistical process control techniques requires for operational definitions and precise measurements. Nevertheless, very often analysts can dispose of process data available only by linguistic terms, that would be a waste to neglect just because of their intrinsic vagueness. Thus a hybrid approach, which integrates fuzzy set theory and common statistical tools, sounds useful in order to improve effectiveness of statistical process control in such a case. In this work, a fuzzy approach is adopted to manage linguistic information, and the use of a Chi-squared control chart is proposed to monitor process performance.

• The Korean Journal of Applied Statistics
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• v.35 no.6
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• pp.725-737
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• 2022

In the standard assumption of statistical process monitoring (SPM) under consideration, the in-control region of the control parameter of quality characteristic consists of a single point. However, if small deviations from the ideal situation may not be of practical importance, the parametric space can consist of three regions: In-control, indifference, and out-of-control. In this paper, we propose two exponentially weighted moving average (EWMA) charting procedures applicable to the situation with three parameter regions, and compare the efficiency of the proposed procedures with the Shewhart chart and the cumulative sum (CUSUM) chart.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.1
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• pp.61-70
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• 2013

This paper presents the response calibration method based on quality control range, which is established from the concept and method of statistical quality control for natural frequency ratio and response ratio. To this end, statistical analysis including descriptive statistics analysis, normality test, ANOVA were performed for response characteristics obtained from loading tests and structural analysis for more than hundred and thirty well-conditioned bridges. Suggested method is based on real structural integrity evaluation case studies and statistical quality control approach, in this respect it is expected to provide scientific criteria and systematic procedure for response calibration and load carrying capacity assessment.

• Proceedings of the Korean Society for Quality Management Conference
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• 1998.11a
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• pp.217-224
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• 1998

A cost model, controlling multiple dependent subprocesses with minimum cost, is derived by renewal theory approach. The optimal multiple cause-selecting control chart and individual Y control chart are thus constructed to monitor the specific product quality and overall product quality contributed by the multiple dependent subprocesses. They may be used to maintain the process with minimum cost and effectively distinguish which component of the subprocesses is out of control. The optimal design parameters of the proposed control charts can be determined by minimizing the cost model using simple grid search method, An example is given to illustrate the application of the optimal multiple cause-selecting control chart and individual Y control chart.

• The Korean Journal of Applied Statistics
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• v.9 no.1
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• pp.91-109
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• 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 Korean Society for Quality Management
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• v.15 no.2
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• pp.10-19
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• 1987

Statistical control charts are useful tools to monitor and control the manufacturing processes and are widely used in most Korean industries. Many Korean companies, however, do not always obtain desired results from the traditional control charts by Shewhart such as the $\overline{X}$-chart, X-chart, $\widetilde{X}$-chart, etc. This is partly because the quality charterstics of the process are not distributed normally but are skewed due to the intermittent production, small lot size, etc. In the Shewhart $\overline{X}$-chart, which is the most widely used one in Korea, such skewed distributions make the plots to be inclined below or above the central line or outside the control limits although no assignable causes can be found. To overcome such shortcomings in nonnormally distributed processes, a distribution-free type of confidence interval can be used, which should be based on order statistics. This thesis is concerned with the design of control chart based on a sample median which is easy to use in practical situation and therefore properties for nonnormal distributions may be easily analyzed. Control limits and central lines are given for the more famous nonnormal distributions, such as Gamma, Beta, Lognormal, Weibull, Pareto, and Truncated-normal distributions.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.3
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• pp.50-60
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• 1998

This paper is concerned with the design of two residual control charts for real-time monitoring of the continuous flow processes. Two different control charts are designed under the situation that observations are correlated each other. Kalman-Filter based model estimation is employed when the process model is known. A black-box approach, based on Back-Propagation Neural Network, is also applied for the design of control chart when there is no prior information of process model. Performance of the designed control charts and traditional control charts is evaluated. Average run length(ARL) is adopted as a criterion for comparison. Experimental results show that the designed control chart using the Neural Network's modeling has shorter ARL than that of the other control charts when process mean is shifted. This means that the designed control chart detects the out-of-control state of the process faster than the others. The designed control chart using the Kalman-Filter based model estimation also has better performance than traditional control chart when process is out-of-control state.

• The Korean Journal of Applied Statistics
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• v.33 no.2
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• pp.161-170
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• 2020

The combined ${\bar{X}}-S^2$ chart is a traditional control chart for simultaneously detecting mean and variance. Control limits for the combined ${\bar{X}}-S^2$ chart are determined so that each chart has the same individual false alarm rate while maintaining the required false alarm rate for the combined chart. In this paper, we provide flexibility to allow the two charts to have different individual false alarm rates as well as evaluate the effect of flexibility. The individual false alarm rate of the ${\bar{X}}$ chart is taken to be γ times the individual false alarm rate of the S² chart. To evaluate the effect of selecting the value of γ, we use the out-of-control average run length and relative mean index as the performance measure for the combined ${\bar{X}}-S^2$ chart.

• The Korean Journal of Applied Statistics
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• v.10 no.1
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• pp.29-36
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• 1997

Four statistical packages useful for statistical process control are reviewed. MINITAB, RS, SAS, and SPSS are compared in terms of general capability, control chart, process capability, design of experiment and Taguchi method.

• The Journal of Engineering Geology
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• v.25 no.1
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• pp.9-20
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• 2015

A strainmeter goes through a period of instability immediately after installation. To determine the stability of strainmeters installed around the Andong fault zone, South Korea, an x-MR control chart analysis and a T² control chart analysis were conducted. The x-MR control chart analysis used an empirically determined 3σ control limit line to identify abnormal data in recently installed strain gauges. In the T² control chart analysis, the control limit line was set at a confidence of 95%. A comparison of the early stage of measurement with the terminal stage of measurement for three months after installation indicates that stabilization depends on the location and direction of each strain gauge in x-MR control chart analysis. In the T² control chart analysis, the number of values exceeding the control limit line decreased as the terminal stage was approached. Based on these results, it is suggested that the 3σ control limit line of an x-MR control chart can be used as a standard for single gauge stability, and that the 95% confidence limit of a T² control chart analysis could be used as the standard for the stability of multi-gauge strainmeters.