In-control Average Run Length Research Articles

An attribute control chart using discriminant limits for monitoring process under the Weibull distribution

A new efficient process monitoring scheme has been developed for a process under which the quality characteristic follows the Weibull distribution. Recently control charts using discriminant limits for process monitoring for a normal distribution have been developed. But, there are many situations in which the distribution of underlying data is not normal. So, the application of such chart to a skewed distribution like the Weibull distribution may lead to erroneous conclusions. In this paper, an attribute chart using discriminant limits for the Weibull distribution has been developed. The parameters of the proposed chart have been determined by considering the in-control average run lengths. An example using the simulation data has been included for the practical use of the proposed scheme. It has been observed that the proposed chart is efficient for the quick detection of an out-of-control process. A real example from a healthcare area is also added to show the application of the proposed control chart.

Conditional design of the EWMA median chart with estimated parameters

ABSTRACTThe exponentially weighted moving average (EWMA) chart is often designed assuming the process parameters are known. In practice, the parameters are rarely known and need to be estimated from Phase I samples. Different Phase I samples are used when practitioners construct their own control chart's limits, which leads to the “Phase I between-practitioners” variability in the in-control average run length (ARL) of control charts. The standard deviation of the ARL (SDARL) is a good alternative to quantify this variability in control charts. Based on the SDARL metric, the performance of the EWMA median chart with estimated parameters is investigated in this paper. Some recommendations are given based on the SDARL metric. The results show that the EWMA median chart requires a much larger amount of Phase I data in order to reduce the variation in the in-control ARL up to a reasonable level. Due to the limitation of the amount of the Phase I data, the suggested EWMA median chart is designed with the bootstrap method which provides a good balance between the in-control and out-of-control ARL values.

A EWMA control chart based on an auxiliary variable and repetitive sampling for monitoring process location

ABSTRACTThis article develops an exponentially weighted moving average (EWMA) control chart using an auxiliary variable and repetitive sampling for efficient detection of small to moderate shifts in location. A EWMA statistic of a product estimator of the average (which utilities the information of auxiliary variables as well as repetitive sampling) is plotted on the proposed chart. The control chart coefficients of the proposed EWMA chart are determined for two strategic limits known as outer and inner control limits for the target in-control average run length. The performance of the proposed EWMA chart is studied using average run length when a shift occurs in the process average. The efficiency of the developed chart is compared with the competitive existing control charts. The results of the study revealed that proposed EWMA chart is more efficient than others to detect small changes in process mean.

Multioutput least square SVR-based multivariate EWMA control chart: The performance evaluation and application

Autocorrelation leads to a bias estimator of standard control charts. It is important to develop control chart that allows autocorrelation and to evaluate its performance. The objective of this paper is to evaluate the performance of multioutput least square support vector regression (MLS-SVR)-based multivariate exponentially weighted moving average (MEWMA) control chart for monitoring multivariate autocorrelated data. For first order of vector autoregressive (VAR) and first order of vector moving average data, the proposed control chart tends to yield stable in-control average run length at about 200. The proposed control chart becomes more insensitive due to the increase of MEWMA smoothing parameter. In the real application, the proposed method is successfully applied to monitor water turbidity and chlorine residual data in the drinking water manufacturing process.

A New S2Control Chart Using Multiple Dependent State Repetitive Sampling

The combined application of multiple dependent state sampling and the repetitive group sampling (RGS) is an efficient sampling scheme for industrial process monitoring as it combines the advantages of both the sampling schemes. In this paper, a new variance control chart has been proposed, when the interesting quality characteristic follows the normal distribution using the combination of these two efficient sampling schemes called multiple dependent state repetitive sampling. The control chart coefficients and parameters have been estimated through simulation for the in-control process by considering the target in-control average run lengths under different process settings. The efficiency of the proposed chart has been determined by computing the out-of-control ARL for different shift levels. The advantages of the proposed monitoring scheme have been discussed and compared with the existing RGS scheme and the single sampling scheme. A simulated example and a real industrial data have been included to demonstrate the application of the proposed monitoring scheme. It has been observed that the proposed chart is a valuable addition to the toolkit of the quality monitoring personnel.

Design of an attribute np control chart for process monitoring based on repetitive group sampling under truncated life tests

ABSTRACTIn this paper, an attribute control chart under repetitive group sampling is designed for monitoring the production process where the lifetime of the product is considered as quality of the product. We assume that the lifetime follows the Pareto distribution of second kind with known shape parameter. The performance of the proposed chart is evaluated by average run length. The control limits coefficients as well as the repetitive group sampling parameter such as sample size are determined such that the in-control average run length is as close as to the specified average run length. Out-of-control average run length is also reported for different shift constants with corresponding optimal parameters. In addition, performance of proposed control chart is compared with the performance of existing chart. An economical designing of proposed control chart is also discussed.

In-control robustness comparison of different control charts

Control charts are widely used to monitor the process parameters. Proper design structure and implementation of a control chart requires its in-control robustness, otherwise, its performance cannot be fairly observed. It is important to know whether a chart is sensitive to disturbances to the model (e.g. normality under which it is developed) or not. This study, explores the robustness of Mixed EWMA-CUSUM (MEC) control chart for location parameter under different non-normal and contaminated environments and compares it with its counterparts. The robustness of the MEC scheme and counterparts is evaluated by using the run length distributions, and for better assessment not only is in-control average run length (ARL) used, but also standard deviation of run length (SDRL) and different percentiles – that is, 5th, 50th and 95th– are considered. A careful insight is necessary in selection and application of control charts in non-normal and contaminated environments. It is observed that the in-control robustness performance of the MEC scheme is quite good in the case of normal, non-normal and contaminated normal distributions as compared with its competitor’s schemes.

An exact method for designing Shewhart and S2 control charts to guarantee in-control performance

The in-control performance of Shewhart and S2 control charts with estimated in-control parameters has been evaluated by a number of authors. Results indicate that an unrealistically large amount of Phase I data is needed to have the desired in-control average run length (ARL) value in Phase II. To overcome this problem, it has been recommended that the control limits be adjusted based on a bootstrap method to guarantee that the in-control ARL is at least a specified value with a certain specified probability. In this article we present simple formulas using the assumption of normality to compute the control limits and therefore, users do not have to use the bootstrap method. The advantage of our proposed method is in its simplicity for users; additionally, the control chart constants do not depend on the Phase I sample data.

ARL-unbiased geometric and CCCG control charts

ABSTRACTIn order to control increases and decreases in a parameter in a timely fashion, a chart should be set in such way that the average run length (ARL) curve attains a maximum in the in-control situation.This article proposes not only a geometric (or cumulative count of conforming, CCC) chart but also a CCC chart under group inspection (CCCG) for which all out-of-control ARL values are smaller than the in-control ARL and thus provides an improvement on the designs described in L. Zhang et al. (2004) and C. W. Zhang et al. (2012). Moreover, by exploring the notions of uniformly most powerful unbiased tests with randomization probabilities, we are able not only to eliminate the bias of the ARL function of the geometric charts but also to bring their in-control ARL exactly to a prespecified value.Instructive examples are provided to illustrate the efficiency of the proposed charts.

Evaluation of an np control chart under truncated life test using quick switching sampling system

In this paper, we propose a new attribute np control chart for monitoring Weibull distributed mean life of a product using a quick switching sampling system. The proposed control chart consists of two pairs of control limits, namely normal and tightened control limits. The optimal parameters of the proposed control chart, such as coefficients of control limits and experiment termination ratio, are determined so that the average run length (ARL) is close to the target in-control ARL. The ARL is calculated for various shift constants for the corresponding determined parameters. The performance of the proposed control chart is evaluated and compared with other existing charts in terms of ARL.

Signed sequential rank CUSUMs

CUSUMs based on the signed sequential ranks of observations are developed for detecting location and scale changes in symmetric distributions. The CUSUMs are distribution-free and fully self-starting: given a specified in-control median and nominal in-control average run length, no parametric specification of the underlying distribution is required in order to find the correct control limits. If the underlying distribution is normal with unknown variance, a CUSUM based on the Van der Waerden signed rank score produces out-of-control average run lengths that are commensurate with those produced by the standard CUSUM for a normal distribution with known variance. For heavier tailed distributions, use of a CUSUM based on the Wilcoxon signed rank score is indicated. The methodology is illustrated by application to real data from an industrial environment.

In-control performance of the joint Phase II − S control charts when parameters are estimated

ABSTRACTThe issue of the effects of parameter estimation on the in-control performance of control charts has motivated researchers for several decades. In this context, recently, acknowledging what has been called by some the practitioner-to-practitioner variability, a new perspective has been advocated, namely, the study of the conditional distribution of the in-control average run length (or the conditional false-alarm rate), which is more meaningful in practice. Adopting this new perspective, some authors have analyzed the conditional distribution of the false-alarm rate (or of the in-control average run length) of and of S charts separately. However, since the and S charts are not typically used separately but together or jointly in many applications, here we study the effects of parameter estimation on the performance of the two charts applied jointly (called the joint charts). For the joint charts, defining the joint false-alarm rate as the probability that at least one of the two charts ( and S) issues a false alarm, we obtain its conditional distribution, some quantiles of interest (upper prediction bounds for it) and the number of Phase I samples required to guarantee that with a high probability the conditional joint false alarm rate will not exceed a maximum tolerated value. We assume normality, consider Sp (the square root of the pooled variance) as the Phase I estimator of the process standard deviation, and consider two possibilities regarding the chart: (1) centered at and (2) centered at a specified target value. The results show (and we formally prove) that, whereas the required number of Phase I samples may be very large for the joint charts, interestingly, it lies between the corresponding numbers of samples required by the chart and by the S chart individually; so, considering the performance of the charts from the perspective of their joint use may slightly alleviate the required number of Phase I samples.

A control chart for monitoring process variation using multiple dependent state sampling

ABSTRACTA control chart for monitoring process variation by using multiple dependent state (MDS) sampling is constructed in the present article. The operational formulas for in-control and out-of-control average run lengths (ARLs) are derived. Control constants are established by considering the target in-control ARL at a normal process. The extensive ARL tables are reported for various parameters and shifted values of process parameters. The performance of the proposed control chart has been evaluated with several existing charts in regard of ARLs, which empowered the presented chart and proved far better for timely detection of assignable causes. The application of the proposed concept is illustrated with a real-life industrial example and a simulation-based study to elaborate strength of the proposed chart over the existing concepts.

An EWMA chart for monitoring the covariance matrix of a multivariate process based on dissimilarity index

AbstractIn this article, we propose an exponentially weighted moving average (EWMA) control chart for monitoring the covariance matrix of a multivariate process based on the dissimilarity index of 2 matrices. The proposed control chart essentially monitors the covariance matrix by comparing the individual eigenvalues of the estimated EWMA covariance matrix with those of the estimated covariance matrix from the in‐control (IC) phase I data. It is different from the conventional EWMA charts for monitoring the covariance matrix, which are either based on comparing the sum or product or both of the eigenvalues of the estimated EWMA covariance matrix with those of the IC covariance matrix. We compare the performance of the proposed chart with that of the best existing chart under the multivariate normal process. Furthermore, to prevent the control limit of the proposed EWMA chart developed using the limited IC phase I data from having extensively excessive false alarms, we use a bootstrap resampling method to adjust the control limit to guarantee that the proposed chart has the actual IC ARL(average run length) not less than the nominal level with a certain probability. Finally, we use an example to demonstrate the applicability and implementation of the proposed EWMA chart.

Shewhart control charts for dispersion adjusted for parameter estimation

ABSTRACTSeveral recent studies have shown that the number of Phase I samples required for a Phase II control chart with estimated parameters to perform properly may be prohibitively high. Looking for a more practical alternative, adjusting the control limits has been considered in the literature. We consider this problem for the classic Shewhart charts for process dispersion under normality and present an analytical method to determine the adjusted control limits. Furthermore, we examine the performance of the resulting chart at signaling increases in the process dispersion. The proposed adjustment ensures that a minimum in-control performance of the control chart is guaranteed with a specified probability. This performance is indicated in terms of the false alarm rate or, equivalently, the in-control average run length. We also discuss the tradeoff between the in-control and out-of-control performance. Since our adjustment is based on exact analytical derivations, the recently suggested bootstrap method is no longer necessary. A real-life example is provided in order to illustrate the proposed methodology.

Double moving average–EWMA control chart for exponentially distributed quality

ABSTRACTA control chart is an ever-popular tool for monitoring the production process. The early detection of a process shift, if any, is the desire of the quality control personnel. In this article, an effective alternative control charting procedure has been developed for the monitoring of exponentially distributed quality characteristic using the double moving average combined with EWMA statistic. The performance of the proposed control chart is examined for different combinations of the shift constant, the EWMA smoothing parameter, the moving average span, and the target in-control average run lengths. It has been observed that the proposed control chart is more efficient in the detection of process shifts as compared to control chart suggested by Khoo and Wang for the same purpose. The proposed control chart is illustrated for practical usage with the help of a synthetic and a real dataset.

Guaranteed In-Control Performance for the Shewhart X and X Control Charts

When in-control parameters are unknown, they have to be estimated using a reference sample. Due to the use of different reference samples in phase I, the control chart performance in phase II will vary across practitioners. This variation is especially large for small sample sizes. To prevent low in-control average run lengths, new corrections for Shewhart control charts are proposed that guarantee a minimum in-control performance with a specified probability. However, a minimum in-control performance guarantee generally lowers the out-of-control performance. To balance the tradeoff between in-control and out-of-control performance, the minimum performance threshold and specified probability can be adjusted as desired. The corrections are given in a closed form so that the bootstrap method, which has recently been suggested, is no longer required. The performance of our proposed correction is illustrated by simulating some practical situations. Furthermore, a comparison is made with tolerance intervals and self-starting control charts.

A Control Chart for Gamma Distribution using Multiple Dependent State Sampling

In this article, a control chart based on multiple dependent (or deferred) state sampling for the gamma distributed quality characteristic is proposed using the gamma to normal transformation. The proposed control chart has two pairs of control limits, which can be determined by considering the in-control average run length (ARL). The shift in the scale parameter of a gamma distribution is considered and the out-of-control ARL is evaluated. The performance of the proposed chart has been shown for different levels of the parameters of the proposed control chart. It is also shown that the proposed chart is better than the Shewhart chart in terms of ARLs. A case study with a real data has been included for the practical usage of the proposed scheme.

A Control Chart for Gamma Distributed Variables Using Repetitive Sampling Scheme

In this manuscript, a control chart is designed when the quality characteristic of interest follows a gamma distribution using repetitive sampling. The Wilson-Hilferty approximation is used to transform the gamma distributed characteristic to a normal random variable. Two pairs of control limits are established and their control constants are determined by considering the specified in-control average run length (ARL). The out-of-control ARL is derived when the process is shifted in terms of the scale parameter of the Gamma distribution. The ARLs are presented for various values of the shape parameter according to process shift parameters. A simulated example is given to illustrate the proposed control chart.

Phase II control charts for monitoring dispersion when parameters are estimated

ABSTRACTShewhart control charts are among the most popular control charts used to monitor process dispersion. To base these control charts on the assumption of known in-control process parameters is often unrealistic. In practice, estimates are used to construct the control charts and this has substantial consequences for the in-control and out-of-control chart performance. The effects are especially severe when the number of Phase I subgroups used to estimate the unknown process dispersion is small. Typically, recommendations are to use around 30 subgroups of size 5 each. We derive and tabulate new corrected charting constants that should be used to construct the estimated probability limits of the Phase II Shewhart dispersion (e.g., range and standard deviation) control charts for a given number of Phase I subgroups, subgroup size and nominal in-control average run-length (ICARL). These control limits account for the effects of parameter estimation. Two approaches are used to find the new charting constants, a numerical and an analytic approach, which give similar results. It is seen that the corrected probability limits based charts achieve the desired nominal ICARL performance, but the out-of-control average run-length performance deteriorate when both the size of the shift and the number of Phase I subgroups are small. This is the price one must pay while accounting for the effects of parameter estimation so that the in-control performance is as advertised. An illustration using real-life data is provided along with a summary and recommendations.