• The Journal of Engineering Geology
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• v.24 no.2
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• pp.191-199
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• 2014

We describe a method for detecting slope instability in flume tests using pore pressure and water content data in conjunction with a statistical control chart analysis. Specifically, we conducted univariate statistical analysis on x-MR control chart data (pore pressure and water content) collected at several points along the flume slope, which we separated into three parts: upper, middle, and lower. To assess our results in the context of landslide forecasting and warning systems, we applied control limit lines at $1{\sigma}$, $2{\sigma}$, and $3{\sigma}$ levels of uncertainty. In doing so, we observed that dispersion time varies depending on the control limit line used. Moreover, the detection of instabilities is highly dependent on the position and type of sensor. Our findings indicate that different characteristics of the data on various factors predict slope failure differently and these characteristics can be identified by univariate statistical analysis. Therefore, we suggest that a univariate statistical approach is an effective method for the early detection of slope instability.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.2
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• pp.56-62
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• 2013

Control chart is representative tool of Statistical Process Control (SPC). But, it is not given information about the economic loss that occurs when a product is produced characteristic value does not match the target value of the process. In order to manage the process, we should consider not only stability of the variation also produce products with a high degree of matching the target value that is most ideal quality characteristics. There is a need for process control in consideration of economic loss. In this paper, we design a new control chart using the quadratic loss function of Taguchi. And we demonstrate effectiveness of new control chart by compare its ARL with ${\overline{x}}-R$ control chart.

• Industrial Engineering and Management Systems
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• v.8 no.3
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• pp.139-147
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• 2009

Control charts are used to distinguish between chance and assignable causes in the variability of quality characteristics. When a control chart signals that an assignable cause is present, process engineers must initiate a search for the assignable cause of the process disturbance. Identifying the time of a process change could lead to simplifying the search for the assignable cause and less process down time, as well as help to reduce the probability of incorrectly identifying the assignable cause. The change point estimation by likelihood theory and the built-in change point estimation in a control chart have been discussed until now. In this article, we discuss two kinds of process change point estimation when the CUSUM ($\bar{x}$, s) control chart for monitoring process mean and variance simultaneously is operated. Throughout some numerical experiments about the performance of the change point estimation, the change point estimation techniques in the CUSUM ($\bar{x}$, s) control chart are considered.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.45
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• pp.265-277
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• 1998

The control chart has been used widely and importantly as a tool for statistical process control(SPC). Most companies are concerned with improving the quality and the productivity as well as reducing the cost, especially in today's highly competitive environment. Though SPC is known as a technique for consistent quality, it is not used properly due to lack of knowledge about it. It is required to develop a support system for control chart selection and interpretation that can be utilized by non-specialist without hard training or experiences. The support system was developed by applying the expert system tool to popular control charts. Though some researches on this area has been performed, the implemented results expose many problems in field applications due to the unsatisfactory explanation of the selected control chart and limited knowledge base for resolving the problems. This thesis presented an expert system for control chart as solution for these problems. The expert system for the control chart selection and interpretation is developed by using Turbo C and EXSYS which is an expert system development tool.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.48
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• pp.65-71
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• 1998

Control chart is a very extensively used tool in testing whether a process is in a state of statistical control or not. In this paper, a robust control chart for variables is proposed, which is based on the Huber's M-estimator. The Huber's M-estimator is a well-known robust estimator in sense of distributional robustness. In the proposed chart, the estimation of the process deviation is modified to have a stable level and high power. To compare the performances of the proposed control chart with the classical (equation omitted), some Monte Carlo simulations are performed. The simulation results show that the robust control chart has good performance.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.26 no.3
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• pp.39-49
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• 2003

Statistical process cintrol is intended to assist operators of a stable system in monitoring whether a change has occurred in the process, and it uses several control charts as main tools. In design and use of control chart, it is rational that probability of false alarm is minimized in stable process and probability of detecting shifts is maximized in out-of-control. In this study, we establish bootstrap control limits for robust M-estimator chart by applying the bootstrap method, called resampling, which could not demand assumptions about pre-distribution when the process is skewed and/or the normality assumption is doubt. The results obtained in this study are summarized as follows : bootstrap M-estimator control chart is developed for applying bootstrap method to M-estimator chart, which is more robust to keep ARL when process contain contaminate quality characteristic.

• Journal of Korean Society for Quality Management
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• v.35 no.1
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• pp.24-34
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• 2007

This paper investigates the average run length (ARL) of a selectively moving average (S-MA) control chart. The S-U chart is designed to detect shifts in the process mean. The basic idea of the S-MA chart is to accumulate previous samples selectively in order to increase the sensitivity. The ARL of the S-MA chart was shown to be monotone decreasing with respect to the decision length in a previous research [3]. This paper derives the steady-state ARL in a closed-form and shows that the monotone property is resulted from head-start assumption. The steady-state ARL is shown to be a sum of head-start ARL and an additional term. The statistical design procedure for the S-MA chart is revised according to this result. Sensitivity study shorts that the steady-state ARL performance is still better than the CUSUM chart or the Exponentially Weighted Moving Average (EWMA) chart.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.11
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• pp.82-90
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• 1995

This paper pressents a condition-based maintenance (CBM) method through bibration analysis. The well known frequency analysis is employed for performing machine fault diagnosis. The statistical control chart is also applied for analyzing the trend of the bearing wear. Vibration sensors are attached to prototype machine and signals are continuously monitored. The sampled data are utilized to evaluate how well the fast fourier transform(FFT) and the statistical control chart techniques could be used to identify defects of machine and to analyze the machine degradation. Experimental results show that the propowed approach could classify every mal-function and could be utilized for real machine diagnosis system.

• Communications for Statistical Applications and Methods
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• v.29 no.4
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• pp.487-498
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• 2022

Social network analysis (SNA) techniques have recently been developed to monitor and detect abnormal behaviors in social networks. As a useful tool for process monitoring, control charts are also useful for network monitoring. In this paper, the degree and closeness centrality measures, in which each has global and local perspectives, respectively, are applied to an exponentially weighted moving average (EWMA) chart and a multinomial cumulative sum (CUSUM) chart for monitoring undirected weighted networks. In general, EWMA charts monitor only one variable in a single chart, whereas multinomial CUSUM charts can monitor a categorical variable, in which several variables are transformed through classification rules, in a single chart. To monitor both degree centrality and closeness centrality simultaneously, we categorize them based on the average of each measure and then apply to the multinomial CUSUM chart. In this case, the global and local attributes of the network can be monitored simultaneously with a single chart. We also evaluate the performance of the proposed procedure through a simulation study.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.1
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• pp.43-51
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• 2014

Statistical process control (SPC) is an important technique for monitoring and managing the manufacturing process. In spite of its easiness and effectiveness, some problematic sides of application exist such that the SPC techniques are hardly reflect the changes of the process conditions. Especially, update of control limits at the right time plays an important role in acquiring a reasonable performance of control charts. Therefore, we propose the control chart performance evaluation index (CPEI) based on count data model to monitor and manage the performance of control charts. The CPEI could indicate the degree of control chart performance and be helpful to detect the proper update cycle of control limits in real time. Experiments using real manufacturing data show that the proper update intervals are made by proposed method.