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Control Charts (control + chart)

Distribution by Scientific Domains

Medical Sciences	11%
Mathematics and Statistics	11%
Chemistry	6%

Selected Abstracts

A Bootstrap Control Chart for Weibull Percentiles

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 2 2006
Michele D. Nichols
Abstract The problem of detecting a shift of a percentile of a Weibull population in a process monitoring situation is considered. The parametric bootstrap method is used to establish lower and upper control limits for monitoring percentiles when process measurements have a Weibull distribution. Small percentiles are of importance when observing tensile strength and it is desirable to detect their downward shift. The performance of the proposed bootstrap percentile charts is considered based on computer simulations, and some comparisons are made with an existing Weibull percentile chart. The new bootstrap chart indicates a shift in the process percentile substantially quicker than the previously existing chart, while maintaining comparable average run lengths when the process is in control. An illustrative example concerning the tensile strength of carbon fibers is presented. Copyright © 2005 John Wiley & Sons, Ltd. [source]

Design Strategies for the Multivariate Exponentially Weighted Moving Average Control Chart

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 6 2004
Murat Caner Testik
Abstract The multivariate exponentially weighted moving average (MEWMA) control chart has received significant attention from researchers and practitioners because of its desirable properties. There are several different approaches to the design of MEWMA control charts: statistical design; economic,statistical design; and robust design. In this paper a review and comparison of these design strategies is provided.Copyright © 2004 John Wiley & Sons, Ltd. [source]

Statistical Process Control Charts for Measuring and Monitoring Temporal Consistency of Ratings

JOURNAL OF EDUCATIONAL MEASUREMENT, Issue 1 2010
M. Hafidz Omar
Methods of statistical process control were briefly investigated in the field of educational measurement as early as 1999. However, only the use of a cumulative sum chart was explored. In this article other methods of statistical quality control are introduced and explored. In particular, methods in the form of Shewhart mean and standard deviation charts are introduced as techniques for ensuring quality in a measurement process for rating performance items in operational assessments. Several strengths and weaknesses of the procedures are explored with illustrative real and simulated rating data. Further research directions are also suggested. [source]

Control Charts for Monitoring Field Failure Data

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 7 2006
Robert G. Batson
Abstract One responsibility of the reliability engineer is to monitor failure trends for fielded units to confirm that pre-production life testing results remain valid. This research suggests an approach that is computationally simple and can be used with a small number of failures per observation period. The approach is based on converting failure time data from fielded units to normal distribution data, using simple logarithmic or power transformations. Appropriate normalizing transformations for the classic life distributions (exponential, lognormal, and Weibull) are identified from the literature. Samples of size 500 field failure times are generated for seven different lifetime distributions (normal, lognormal, exponential, and four Weibulls of various shapes). Various control charts are then tested under three sampling schemes (individual, fixed, and random) and three system reliability degradations (large step, small step, and linear decrease in mean time between failures (MTBF)). The results of these tests are converted to performance measures of time to first out-of-control signal and persistence of signal after out-of-control status begins. Three of the well-known Western Electric sensitizing rules are used to recognize the assignable cause signals. Based on this testing, the ,X -chart with fixed sample size is the best overall for field failure monitoring, although the individual chart was better for the transformed exponential and another highly-skewed Weibull. As expected, the linear decrease in MTBF is the most difficult change for any of the charts to detect. Copyright © 2005 John Wiley & Sons, Ltd. [source]

Designing Accurate Control Charts Based on the Geometric and Negative Binomial Distributions

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 8 2005
Neil C. Schwertman
Abstract Attribute control charts are used effectively to monitor for process change. Their accuracy can be improved by judiciously selecting the sample size. The required sample sizes to achieve accuracy can be quite restrictive, especially when the nominal proportions of non-conforming units are quite small. The usual attribute control chart has a set sample size and the number of non-conforming units in the sample is plotted. If, instead of setting a specific sample size the number of non-conforming units is set, an alternative monitoring process is possible. Specifically, the cumulative count of conforming (CCC- r) control chart is a plot of the number of units that must be tested to find the rth non-conforming unit. These charts, based on the geometric and negative binomial distributions, are often suggested for monitoring very high quality processes. However, they can also be used very efficiently to monitor processes of lesser quality. This procedure has the potential to find process deterioration more quickly and efficiently. Xie et al. (Journal of Quality and Reliability Management 1999; 16(2):148,157) provided tables of control limits for CCC- r charts for but focused mainly on high-quality processes and the tables do not include any assessments of the risk of a false alarm or the reliability of detecting process change. In this paper, these tables are expanded for processes of lesser quality and include such assessments using the number of expected monitoring periods (average run lengths (ARLs)) to detect process change. Also included is an assessment of the risk of a false alarm, that is, a false indication of process deterioration. Such assessments were not included by Xie et al. but are essential for the quality engineer to make sound decisions. Furthermore, a hybrid of the control charts based on the binomial, geometric and negative binomial distributions is proposed to monitor for process change. Copyright © 2005 John Wiley & Sons, Ltd. [source]

Optimal Design of VSI ,X Control Charts for Monitoring Correlated Samples

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 8 2005
Yan-Kwang Chen
Abstract This paper develops an economic design of variable sampling interval (VSI),X control charts in which the next sample is taken sooner than usual if there is an indication that the process is off-target. When designing VSI,X control charts, the underlying assumption is that the measurements within a sample are independent. However, there are many practical situations that violate this hypothesis. Accordingly, a cost model combining the multivariate normal distribution model given by Yang and Hancock with Bai and Lee's cost model is proposed to develop the design of VSI charts for correlated data. An evolutionary search method to find the optimal design parameters for this model is presented. Also, we compare VSI and traditional ,X charts with respect to expected cost per unit time, utilizing hypothetical cost and process parameters as well as various correlation coefficients. The results indicate that VSI control charts outperform the traditional control charts for larger mean shift when correlation is present. In addition, there is a difference between the design parameters of VSI charts when correlation is present or absent. Copyright © 2005 John Wiley & Sons, Ltd. [source]

Model Inadequacy and Residuals Control Charts for Autocorrelated Processes

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 2 2005
Murat Caner Testik
Abstract As a result of time series parameter estimation based on previous data, the probability content of residuals control charts may vary when standard control limits are used. In this paper, we consider the AR(1) process with the autoregressive parameter being estimated from a sample of observations. The performance of the exponentially weighted moving average (EWMA) control chart for residuals is investigated. Modified control limits that account for the uncertainty in the parameter estimate are provided. Comparisons through simulation signify the importance of the modified control limits. Copyright © 2004 John Wiley & Sons, Ltd. [source]

Identifying the time of polynomial drift in the mean of autocorrelated processes

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 5 2010
Marcus B. Perry
Abstract Control charts are used to detect changes in a process. Once a change is detected, knowledge of the change point would simplify the search for and identification of the special ause. Consequently, having an estimate of the process change point following a control chart signal would be useful to process engineers. This paper addresses change point estimation for covariance-stationary autocorrelated processes where the mean drifts deterministically with time. For example, the mean of a chemical process might drift linearly over time as a result of a constant pressure leak. The goal of this paper is to derive and evaluate an MLE for the time of polynomial drift in the mean of autocorrelated processes. It is assumed that the behavior in the process mean over time is adequately modeled by the kth-order polynomial trend model. Further, it is assumed that the autocorrelation structure is adequately modeled by the general (stationary and invertible) mixed autoregressive-moving-average model. The estimator is intended to be applied to data obtained following a genuine control chart signal in efforts to help pinpoint the root cause of process change. Application of the estimator is demonstrated using a simulated data set. The performance of the estimator is evaluated through Monte Carlo simulation studies for the k=1 case and across several processes yielding various levels of positive autocorrelation. Results suggest that the proposed estimator provides process engineers with an accurate and useful estimate for the last sample obtained from the unchanged process. Copyright © 2009 John Wiley & Sons, Ltd. [source]

A clustering approach to identify the time of a step change in Shewhart control charts

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 7 2008
Mehdi Ghazanfari
Abstract Control charts are the most popular statistical process control tools used to monitor process changes. When a control chart indicates an out-of-control signal it means that the process has changed. However, control chart signals do not indicate the real time of process changes, which is essential for identifying and removing assignable causes and ultimately improving the process. Identifying the real time of the change is known as the change-point estimation problem. Most of the traditional methods of estimating the process change point are developed based on the assumption that the process follows a normal distribution with known parameters, which is seldom true. In this paper, we propose clustering techniques to estimate Shewhart control chart change points. The proposed approach does not depend on the true values of the parameters and even the distribution of the process variables. Accordingly, it is applicable to both phase-I and phase-II of normal and non-normal processes. At the end, we discuss the performance of the proposed method in comparison with the traditional procedures through extensive simulation studies. Copyright © 2008 John Wiley & Sons, Ltd. [source]

Relationships Among Control Charts Used with Feedback Control

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 8 2006
George Runger
Abstract Feedback control is common in modern manufacturing processes and there is a need to combine statistical process control in such systems. Typical types of assignable causes are described and fault signatures are calculated. A fault signature can be attenuated by the controller and an implicit confounding among faults of different types is discussed. Furthermore, the relationships between various control statistics are developed. Control charts have been proposed previously for deviations from target and for control adjustments. We describe why one or the other can be effective in some cases, but that neither directly incorporates the magnitude (or signature) of an assignable cause. Various disturbance models and control schemes, both optimal and non-optimal, are included in a mathematically simple model that obtains results through properties of linear filters. We provide analytical results for a widely-used model of feedback control. Copyright © 2006 John Wiley & Sons, Ltd. [source]

Control charts: a cost-optimization approach for processes with random shifts

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 3 2004
András Zempléni
Abstract In this paper we describe an approach for establishing control limits and sampling times which derives from economic performance criteria and a model for random shifts. The total cost related to both production and control is calculated, based on cost estimates for false alarms, for not identifying a true out of control situation, and for obtaining a data record through sampling. We describe the complete process for applying the method and compare with conventional procedures to real data from a Portuguese pulp and paper industrial plant. It turns out that substantial cost-reductions may be obtained. Copyright © 2004 John Wiley & Sons, Ltd. [source]

Monitoring processes with data censored owing to competing risks by using exponentially weighted moving average control charts

JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 3 2001
Stefan H. Steiner
In industry, process monitoring is widely employed to detect process changes rapidly. However, in some industrial applications observations are censored. For example, when testing breaking strengths and failure times often a limited stress test is performed. With censored observations, a direct application of traditional monitoring procedures is not appropriate. When the censoring occurs due to competing risks, we propose a control chart based on conditional expected values to detect changes in the mean strength. To protect against possible confounding caused by changes in the mean of the censoring mechanism we also suggest a similar chart to detect changes in the mean censoring level. We provide an example of monitoring bond strength to illustrate the application of this methodology. [source]

Duncan's model for X, -control charts: sensitivity analysis to input parameters

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 1 2010
Cinzia Mortarino
Abstract Duncan's model is a well-known procedure to build a control chart with specific reference to the production process it has to be applied to. Although many papers report true applications proving the procedure's noteworthy economic advantages over control charts set purely on the basis of standard statistical criteria, this method is often perceived only as an academic exercise. Perhaps the greater barrier preventing its practical application stems from the difficulty in making cost items explicit. In this paper a sensitivity analysis is proposed for misspecification in the cost parameters for optimal solutions of Duncan's model. While similar contributions published in the literature perform sensitivity analyses with a one-factor-at-a-time scheme, the original contribution of this paper is represented by the focus given on interactions among changes in values of different cost parameters. The results obtained here denote that all factors significantly affect optimal solutions through quite complicated interactions. This should not, in our opinion, discourage the implementation of Duncan's model, pointing conversely to its robust versions, already available in the current literature. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Adaptive charting schemes based on double sequential probability ratio tests

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 1 2009
Yan Li
Abstract Sequential probability ratio test (SPRT) control charts are shown to be able to detect most shifts in the mean or proportion substantially faster than conventional charts such as CUSUM charts. However, they are limited in applications because of the absence of the upper bound on the sample size and possibly large sample numbers during implementation. The double SPRT (2-SPRT) control chart, which applies a 2-SPRT at each sampling point, is proposed in this paper to solve some of the limitations of SPRT charts. Approximate performance measures of the 2-SPRT control chart are obtained by the backward method with the Gaussian quadrature in a computer program. On the basis of two industrial examples and simulation comparisons, we conclude that the 2-SPRT chart is competitive in that it is more sensitive and economical for small shifts and has advantages in administration because of fixed sampling points and a proper upper bound on the sample size. Copyright © 2008 John Wiley & Sons, Ltd. [source]

A clustering approach to identify the time of a step change in Shewhart control charts

Attribute control charts using generalized zero-inflated Poisson distribution

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 7 2008
Nan Chen
Abstract This paper presents a control charting technique to monitor attribute data based on a generalized zero-inflated Poisson (GZIP) distribution, which is an extension of ZIP distribution. GZIP distribution is very flexible in modeling complicated behaviors of the data. Both the technique of fitting the GZIP model and the technique of designing control charts to monitor the attribute data based on the estimated GZIP model are developed. Simulation studies and real industrial applications illustrate that the proposed GZIP control chart is very flexible and advantageous over many existing attribute control charts. Copyright © 2008 John Wiley & Sons, Ltd. [source]

A one-sided MEWMA chart for health surveillance

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 5 2008
Michael D. Joner Jr
Abstract It is often important to rapidly detect an increase in the incidence rate of a given disease or other medical condition. It has been shown that when disease counts are sequentially available from a single region, a univariate control chart designed to detect rate increases, such as a one-sided cumulative sum chart, is very effective. When disease counts are available from several regions at corresponding times, the most efficient monitoring method is not readily apparent. Multivariate monitoring methods have been suggested for dealing with this detection problem. Some of these approaches have shortcomings that have been recently demonstrated in the quality control literature. We discuss these limitations and suggest an alternative multivariate exponentially weighted moving average chart. We compare the average run-length performance of this chart with that of competing methods. We also evaluate the statistical performance of these charts when the actual increase in the disease count rate is different from the one that the chart was optimized to detect quickly. Copyright © 2008 John Wiley & Sons, Ltd. [source]

Estimating the Change Point of a Poisson Rate Parameter with a Linear Trend Disturbance

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 4 2006
Marcus B. Perry
Abstract Knowing when a process changed would simplify the search and identification of the special cause. In this paper, we compare the maximum likelihood estimator (MLE) of the process change point designed for linear trends to the MLE of the process change point designed for step changes when a linear trend disturbance is present. We conclude that the MLE of the process change point designed for linear trends outperforms the MLE designed for step changes when a linear trend disturbance is present. We also present an approach based on the likelihood function for estimating a confidence set for the process change point. We study the performance of this estimator when it is used with a cumulative sum (CUSUM) control chart and make direct performance comparisons with the estimated confidence sets obtained from the MLE for step changes. The results show that better confidence can be obtained using the MLE for linear trends when a linear trend disturbance is present. Copyright © 2005 John Wiley & Sons, Ltd. [source]

Designing Accurate Control Charts Based on the Geometric and Negative Binomial Distributions

Using Profile Monitoring Techniques for a Data-rich Environment with Huge Sample Size

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 7 2005
Kaibo Wang
Abstract In-process sensors with huge sample size are becoming popular in the modern manufacturing industry, due to the increasing complexity of processes and products and the availability of advanced sensing technology. Under such a data-rich environment, a sample with huge size usually violates the assumption of homogeneity and degrades the detection performance of a conventional control chart. Instead of charting summary statistics such as the mean and standard deviation of observations that assume homogeneity within a sample, this paper proposes charting schemes based on the quantile,quantile (Q,Q) plot and profile monitoring techniques to improve the performance. Different monitoring schemes are studied based on various shift patterns in a huge sample and compared via simulation. Guidelines are provided for applying the proposed schemes to similar industrial applications in a data-rich environment. Copyright © 2005 John Wiley & Sons, Ltd. [source]

Model Inadequacy and Residuals Control Charts for Autocorrelated Processes

Design Strategies for the Multivariate Exponentially Weighted Moving Average Control Chart

An integrated model for statistical and vision monitoring in manufacturing transitions

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 6 2003
Harriet Black Nembhard
Abstract Manufacturing transitions have been increasing due to higher pressures for product variety. One dimension of this variety is color. A major quality control challenge is to regulate the color by capturing data on color in real-time during the operation and to use it to assess the opportunities for good parts. Control charting, when applied to a stable state process, is an effective monitoring tool to continuously check for process shifts or upsets. However, the presence of transition events can impede the normal performance of a traditional control chart. In this paper, we present an integrated model for statistical and vision monitoring using a tracking signal to determine the start of the transition and a confirmation signal to ensure that any process oscillation has concluded. We also developed an automated color analysis and forecasting system (ACAFS) that we can adjust and calibrate to implement this methodology in different production processes. We use a color transition process in plastic extrusion to illustrate a transition event and demonstrate our proposed methodology. Copyright © 2003 John Wiley & Sons, Ltd. [source]

Process monitoring for correlated gamma-distributed data using generalized-linear-model-based control charts

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 6 2003
Duangporn Jearkpaporn
Abstract A model-based scheme is proposed for monitoring multiple gamma-distributed variables. The procedure is based on the deviance residual, which is a likelihood ratio statistic for detecting a mean shift when the shape parameter is assumed to be unchanged and the input and output variables are related in a certain manner. We discuss the distribution of this statistic and the proposed monitoring scheme. An example involving the advance rate of a drill is used to illustrate the implementation of the deviance residual monitoring scheme. Finally, a simulation study is performed to compare the average run length (ARL) performance of the proposed method to the standard Shewhart control chart for individuals. Copyright © 2003 John Wiley & Sons, Ltd. [source]

Capability measures for processes with multiple characteristics

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 2 2003
K. S. Chen
Abstract Process capability indices, such as , , and , have been widely used in the manufacturing industry providing numerical measures on process precision, process accuracy, and process performance. Capability measures for processes with a single characteristic have been investigated extensively. However, capability measures for processes with multiple characteristics are comparatively neglected. In this paper, we consider a generalization of the yield index proposed by Boyles, for processes with multiple characteristics. We establish a relationship between the generalization and the process yield. We also develop a control chart based on the proposed generalization, which displays all the characteristic measures in one single chart. Using the chart, the engineers can effectively monitor and control the performance of all process characteristics simultaneously. Copyright © 2003 John Wiley & Sons, Ltd. [source]

A magnitude-robust control chart for monitoring and estimating step changes for normal process means

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 6 2002
Joseph J. Pignatiello Jr
Abstract Statistical process control charts are intended to assist operators of a usually stable system in monitoring whether a change has occurred in the process. When a change does occur, the control chart should detect it quickly. If the operator can also be provided information that aids in the search for the special cause, then critical off-line time can be saved. We investigate a process-monitoring tool that not only provides speedy detection regardless of the magnitude of the process shift, but also supplies useful change point statistics. A likelihood ratio approach can be used to develop a control chart for permanent step change shifts of a normal process mean. The average run length performance for this chart is compared to that of several cumulative sum (CUSUM) charts. Our performance comparisons show that this chart performs better than any one CUSUM chart over an entire range of potential shift magnitudes. The likelihood ratio approach also provides point and interval estimates for the time and magnitude of the process shift. These crucial change-point diagnostics can greatly enhance special cause investigation. Copyright © 2002 John Wiley & Sons, Ltd. [source]

Controlling jumps in correlated processes of Poisson counts

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 5 2009
Christian H. WeißArticle first published online: 3 DEC 200
Abstract Processes of autocorrelated Poisson counts can often be modelled by a Poisson INAR(1) model, which proved to apply well to typical tasks of SPC. Statistical properties of this model are briefly reviewed. Based on these properties, we propose a new control chart: the combined jumps chart. It monitors the counts and jumps of a Poisson INAR(1) process simultaneously. As the bivariate process of counts and jumps is a homogeneous Markov chain, average run lengths (ARLs) can be computed exactly with the well-known Markov chain approach. Based on an investigation of such ARLs, we derive design recommendations and show that a properly designed chart can be applied nearly universally. This is also demonstrated by a real-data example from the insurance field. Copyright © 2008 John Wiley & Sons, Ltd. [source]

Quality monitoring in thyroid surgery using the Shewhart control chart,

BRITISH JOURNAL OF SURGERY (NOW INCLUDES EUROPEAN JOURNAL OF SURGERY), Issue 2 2009
A. Duclos
Background: A control chart can help to interpret and reduce sources of variability in patient safety by continuously monitoring indicators. The aim of this study was to monitor the outcome of thyroid surgery using control charts. Methods: Patients who had thyroid surgery during 2006,2007 were included in the study. Safety was monitored based on postoperative complications of recurrent laryngeal nerve palsy and hypocalcaemia. Indicators were extracted prospectively from the hospital information system and plotted each month on a P-control chart. Performance of the surgical team was also measured retrospectively for 2004,2005 (baseline period) to compare surgical outcomes before and after control chart implementation. Electromyographic monitoring of recurrent laryngeal nerves was not used, nor was calcium or vitamin D given routinely. Results: The outcomes of 1114 thyroid procedures were monitored. Although the proportion of patients with recurrent laryngeal nerve palsy was similar for baseline and monitored periods (6·4 and 7·2 per cent respectively), there was a 35·3 per cent decrease in hypocalcaemia after implementation of control charts (P < 0·001). Complications almost doubled during a period when one surgeon was away and operating room renovations took place. Conclusion: Outcome monitoring in thyroid surgery using control charts is useful for identifying potential issues in patient safety. Copyright © 2009 British Journal of Surgery Society Ltd. Published by John Wiley & Sons, Ltd. [source]

Benchmarks and control charts for surgical site infections

BRITISH JOURNAL OF SURGERY (NOW INCLUDES EUROPEAN JOURNAL OF SURGERY), Issue 7 2000
T. L. Gustafson
Background Although benchmarks and control charts are basic quality improvement tools, few surgeons use them to monitor surgical site infection (SSI). Obstacles to widespread acceptance include: (1) small denominators, (2) complexities of adjusting for patient risk and (3) scepticism about their true purpose (cost cutting, surgical privilege determination or improving outcomes). Methods The application of benchmark charts (using US national SSI rates as limits) and control charts (using facility rates as limits) was studied in 51 hospitals submitting data to the AICE National Database Initiative. SSI rates were risk adjusted by calculating a new statistic, the standardized infection ratio (SIR), based on the risk index suggested by the Centers for Disease Control National Nosocomial Infection Surveillance Study. Fourteen different types of control chart were examined and 115 suspiciously high or low monthly rates were flagged. Participating hospital epidemiologists investigated and classified each flag as ,a real problem' (potentially preventable) or ,not a problem' (beyond the control of personnel at this facility). Results None of the standard, widely recommended, control charts studied showed practical value for identifying either preventable rate increases or outbreaks (clusters due to a single organism). On the other hand, several types of risk-adjusted control chart based on the SIR correctly identified most true opportunities for improvement. Sensitivity, specificity and receiver,operator characteristic (ROC) analysis revealed that the XmR chart of monthly SIRs would be useful in hospitals with smaller surgical volumes (ROC area = 0·732, P = 0·001). For larger hospitals, the most sensitive and robust SIR chart for real-time monitoring of surgical infections was the mXmR chart (ROC area = 0·753, P = 0·0005). © 2000 British Journal of Surgery Society Ltd [source]

,-Cut fuzzy control charts for linguistic data

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 12 2004
Murat Gülbay
The major contribution of fuzzy set theory is its capability of representing vague data. Fuzzy logic offers a systematic base in dealing with situations that are ambiguous or not well defined. In the literature, there exist some fuzzy control charts developed for linguistic data that are mainly based on membership and probabilistic approaches. In this article, ,-cut control charts for attributes are developed. This approach provides the ability of determining the tightness of the inspection by selecting a suitable ,-level: The higher , the tighter inspection. The article also presents a numerical example and interprets and compares other results with the approaches developed previously. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 1173,1195, 2004. [source]