• Journal of Korean Society for Quality Management
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• v.29 no.1
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• pp.1-10
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• 2001

We consider the problem of detecting special variations in multivariate $T^2$-control chart when two or more multivariate outliers are present. Since a multivariate outlier may reflect slippage in mean, variance, or correlation, it can distort the sample mean vector and sample covariance matrix. Damaged sample mean vector and sample covariance matrix have difficulty in examining special variations clearly, An alternative to detection outliers or special variations is to use robust estimators of mean vector and covariance matrix that are less sensitive to extreme observations than are the standard estimators $\bar{x}$ and $\textbf{S}$. We applied popular minimum volume ellipsoid(MVE) and minimum covariance determinant(MCD) method to estimate mean vector and covariance matrix and compared its results with standard $T^2$-control chart using simulated multivariate data with outliers. We found that the modified $T^2$-control chart based on the above robust methods were more effective in detecting special variations clearly than the standard $T^2$-control chart.

• 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.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.6
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• pp.521-529
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• 2015

Statistical process control techniques have been greatly implemented in industries for improving product quality and saving production costs. As a primary tool among these techniques, control charts are widely used to detect the occurrence of assignable causes. In most works on the control charts it considered the problem of monitoring the mean and variance, and the quality characteristic of interest is normally distributed. In some situations monitoring of the minimum and maximum values is more important and the quality characteristic of interest is the Weibull distribution rather than a normal distribution. In this paper, we consider the statistical design of minimum and maximum control charts when the distribution of the quality characteristic of interest is Weibull. The proposed minimum and maximum control charts are applied to the wind data. The results of the application show that the proposed method is more effective than traditional methods.

• Communications for Statistical Applications and Methods
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• v.17 no.3
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• pp.357-365
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• 2010

This paper considers the integrated process control procedure for detecting special causes in an IMA(1,1) noise process that is being adjusted using a minimum mean squared error adjustment. As a SPC procedure, we use a GLR chart for detecting special causes whose effects are the sustained shift or the sustained drift in the process mean, and the sustained shift in the process variance. For the design of the GLR chart, we derive expressions for the control limit which accurately satisfies the given in-control ARL.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.4
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• pp.203-212
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• 2018

Quality requirements of manufactured products or parts are given in the form of specification limits on the quality characteristics of individual units. If a product is to meet the customer's fitness for use criteria, it should be produced by a process which is stable or repeatable. In other words, it must be capable of operating with little variability around the target value or nominal value of the product's quality characteristic. In order to maintain and improve product quality, we need to apply statistical process control techniques such as histogram, check sheet, Pareto chart, cause and effect diagram, or control charts. Among those techniques, the most important one is control charting. The cumulative sum (CUSUM) control charts have been used in statistical process control (SPC) in industries for monitoring process shifts and supporting online measurement. The objective of this research is to apply Taguchi's quality loss function concept to cost based CUSUM control chart design. In this study, a modified quality loss function was developed to reflect quality loss situation where general quadratic loss curve is not appropriate. This research also provided a methodology for the design of CUSUM charts using Taguchi quality loss function concept based on the minimum cost per hour criterion. The new model differs from previous models in that the model assumes that quality loss is incurred even in the incontrol period. This model was compared with other cost based CUSUM models by Wu and Goel, According to numerical sensitivity analysis, the proposed model results in longer average run length in in-control period compared to the other two models.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.1141-1149
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• 2009

In Bone Mineral Density(BMD) measurements, accuracy and precision must be superior in order to know the small changes in bone mineral density and actual biological changes. Therefore the purpose of this study is to increase the reliability of bone mineral density inspection through appropriate management of image quality from machines and inspectors. For the machine management method, the recommended phantom from each bone mineral density machine manufacturer was used to take 10~25 measurements to determine the standard amount and permitted limit. On each inspection day, measurements were taken everyday or at least three times per week to verify the whether or not change existed in the amount of actual bone mineral density. Also evaluations following Shewhart control chart and CUSUM control chart rules were made for the bone mineral density figures from the phantoms used for measurements. Various forms of management became necessary for machine installation and movement. For the management methods of inspectors, evaluation of the measurement precision was conducted by testing the reproducibility of the exact same figures without any real biological changes occurring during reinspection. There were two measurement methods followed: patients were either measured twice with 30 measurements or three times with 15 measurements. An important point to make regarding measurements is that after the first inspection and any other inspection following, the patient was required to come off the inspection table completely and then get back on for any further measurements. With a 95% confidence level, the precision error produced from the measurement bone mineral figures produced a precision error of 2.77 times the minimum of the biological bone mineral density change (Least significant change: LSC). In order to assure reliability in inspection, there needs to be good oversight of machine management and measurer for machine operation and inspection error. Accuracy error in machines needs to be reduced to under 1% for scientific development in bone mineral density machines.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.428-432
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• 2003

Quantitative Feedback Theory (QFT) is one of most effective methods of robust controller design and can be considered as a suitable method for systems with parametric uncertainties. Particularly it allows us to obtain controllers less conservative than other methods like $H_{\infty}$ and ${\mu}$-synthesis. In QFT method, we transform all the uncertainties and desired specifications to some boundaries in Nichols chart and then we have to find the nominal loop transfer function such that satisfies the boundaries and has the minimum high frequency gain. The major drawback of the QFT method is that there is no effective and useful method for finding this nominal loop transfer function. The usual approach to this problem involves loop-shaping in the Nichols chart by manipulating the poles and zeros of the nominal loop transfer function. This process now aided by recently developed computer aided design tools proceeds by trial and error and its success often depends heavily on the experience of the loop-shaper. Thus for the novice and First time QFT user, there is a genuine need for an automatic loop-shaping tool to generate a first-cut solution. In this paper, we approach the automatic QFT loop-shaping problem by using an algorithm involving Linear Programming (LP) techniques and Genetic Algorithm (GA).

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

• Communications for Statistical Applications and Methods
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• v.16 no.3
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• pp.429-436
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• 2009

This paper considers the integrated process control procedure for detecting special causes in an IMA(1,1) process that is being adjusted automatically after each observation using a minimum mean squared error adjustment policy. When the control chart signals after the occurrence of a special cause, the special cause will be detected and eliminated from the process by the rectifying action. However, when the elimination of the special cause costs high or is not practically possible, an alternative action is to readjust the process with appropriately modified adjustment scheme. In this paper, we propose the readjustment procedure after having a true signal, and show that the use of the readjustment can reduce the deviation of a process from the target.

• Journal of Power Electronics
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• v.16 no.2
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• pp.542-552
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• 2016

Central 60° synchronous modulation is an easy pulse-width modulation (PWM) method to implement for the traction inverters of urban rail trains at a very low switching frequency. Unfortunately, its switching patterns are determined by a Fourier analysis of assumed steady-state voltages. As a result, its transient responses are not very good with over-currents and high instantaneous torque pulses. In the proposed solution, the switching patterns of the conventional central 60° modulation are modified according to the dynamic error between the target and actual stator flux. Then, the specific trajectory of the stator flux and current vector can be guaranteed, which leads to better system transients. In addition, stator flux control is introduced to get smooth mode switching between the central 60° modulation and the other PWMs in this paper. A detailed flow chart of the control signal transmission is given. The target flux is obtained by an integral of the target voltage. The actual PMSM flux is estimated by a minimum order flux state observer based on the extended flux model. Based on a two-level inverter model, improved rules in the α-β stationary coordinate system and equations of the switching patterns amendment are proposed. The proposed method is verified by simulation and experimental results.