Search

In every process of the manufacturing and service industry, there exist variations. Quality practitioners desire to avoid these variations to maintain and improve the quality level of products and services. The control charts are commonly applied under the assumption of normality, however, when the underlying assumption is not valid, nonparametric (NP) control charts become alternatives for quality engineers and practitioners. In this article, an NP progressive mean control chart based on Wilcoxon signed-rank statistic (NPPM-SR) has been proposed for prompt detection of shifts in the process target. The NPPM-SR chart proves in-control robust and much effective performance in spotting deviations in the process location for heavy-tailed and skewed distributions. The run-length distribution performance of the proposed NPPM-SR chart for some selected continuous symmetrical distributions; normal, t, Laplace, logistic and contaminated normal distributions are evaluated under zero- and steady states using average run length (ARL) and some other run-length characteristics. The proposed NPPM-SR chart is compared with existing NP counterparts such as; NP exponentially weighted moving average sign (NPEWMA-SN) chart, NP EWMA based on Wilcoxon signed-rank statistic (NPEWMA-SR) chart and NP cumulative sum based on Wilcoxon signed-rank statistic (NPCUSUM-SR) chart. For the comparative analysis, the parametric competitors such as; traditional EWMA (EWMA- X ¯ ) chart and Double EWMA- X ¯ (DEWMA- X ¯ ) control charts are also included in this study. The proposed NPPM-SR charting scheme under zero-state has been found efficient as compared to steady-state and its existing counterparts. The piston rings data and simulated data have been taken for the illustration of the proposal. [ABSTRACT FROM AUTHOR]

In statistical process control (SPC), the control chart is a quite popular technique to monitor the process efficacy. From a statistical point of view, the control chart is considered superior if it has an effective structure withholding property of the resistance against infrequent situations in a practical environment. The current study is designed for the same purpose for observing the dispersion parameter of log‐normal distribution by using the structure of an exponentially weighted moving average (EWMA) chart. For the extensive study, EWMA range, EWMA standard deviation, EWMA Qn,${Q_n},$ EWMA Sn,${S_n},$ EWMA mean absolute deviation, and EWMA transform standard deviation control charts are proposed. Properties of the run‐length profile of the proposed designed structures based on different existing estimators as well as a newly transformed dispersion estimator for log‐normal standard deviation are evaluated. The results indicate that the newly developed scheme outperforms the competitors when the dispersion parameter of the log‐normal distribution attains a large value. A real‐life application is also provided to validate how the developed design can be used in practice. [ABSTRACT FROM AUTHOR]

The process monitoring techniques play an essential role to improve the overall performance of processes. The control chart is an essential monitoring tool used to detect changes in the process parameters. The Shewhart charts are famous for detecting larger shifts, while exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) control charts are famous for detecting small‐to‐moderate shifts. The separate control schemes are required for the quick identification of the changes in the process parameters because sometimes testing is too expensive or time taking and a practitioner may not afford any kind of defects or loss. With this motivation, the dynamic feature of this article is to introduce an efficient sequential probability ratio test (SPRT) decision‐based Tukey CUSUM design. The performance of the proposal is judged by using several run lengths (RLs) performance measures such as average, median, standard deviation, and percentile RLs. Based on the comparative analysis, it is revealed that the proposed chart offers more sensitivity towards the changes in process location than its competitor's charts for several probability models. The study proposal may find applications in packaging, manufacturing, decision‐making, finance and economics modeling, image processing and automation. A case study from steel rods manufacturing is included to demonstrate the application of the proposed design. [ABSTRACT FROM AUTHOR]

The assumption of normally distributed data is emerged with several statistical inferences as well as in statistical process control. But various real practices on data in different fields like Biological sciences, health, production processes, and manufacturing industries exhibit non‐normal behavior. The current study is concerned with developing robust adaptive exponentially weighted moving average (AEWMA) control charts to monitor the location of non‐normal environments. In the current study, four estimators are considered and listed as Mean (Y¯), Mid‐range (MR), Median (Y∼)and Trimean (TM) for observing process target. Robust proposals of the said schemes are scrutinized towards symmetric non‐normal (t and Laplace) and skewed (Log‐normal and Gamma) environments. The average of run‐length and standard deviation of run‐length are taken as performance evaluation measures. Additionally, some percentile points of distribution run length are also reported for a better understanding of run‐length distribution. Corrected design constants of the proposed charts are also provided for mentioned distributions. Implementation of the proposed schemes is illustrated by providing examples related to real practice. [ABSTRACT FROM AUTHOR]

The homogeneously weighted moving average (HWMA) control chart is famous to identify small deviations in the process mean. The plotting statistic of the HWMA chart assigns equal weight among the previous samples as compared to the plotting statistic of the exponentially weighted moving average chart. We propose a new HWMA chart that uses the plotting statistic of the cumulative sum chart. The run length performance of the proposed chart is measured in terms of the average, the standard deviation, some percentile points, and compared with some existing counterparts' charts. The comparison shows that the proposed chart performs superior to their existing counterparts. An application based on a real‐life dataset is also presented. [ABSTRACT FROM AUTHOR]

In practical situations, the underlying process distribution sometimes deviates from normality and their distribution is partially or completely unknown. In that instance, rather than staying with/depending on the conventional parametric control charts, we consider non‐parametric control charts due to their exceptional performance. In this paper, a new non‐parametric double homogeneously weighted moving average sign control chart is proposed with the least assumptions. This chart is based on a sign test statistic for catching the smaller deviations in the process location. Run‐length (RL) properties of the proposed chart are studied with the help of Monte Carlo simulations. Both in‐control and out‐of‐control RL properties show that the proposed chart is a better contender as compared to some existing charts from the literature. A real‐life application for practical consideration of the proposed chart is also provided. [ABSTRACT FROM AUTHOR]

Toxicant testing is a major component of the environmental solution testing program. This test system aims to evaluate the sensitivity of the test species, evaluate comparability between laboratory test results, and classify possible variability sources such as healthy organisms, organism batches, laboratory water, and food quality changes. For the continuous assessment of sensitivity and accuracy of laboratory toxicity tests, control charts are often used. The cumulative sum (CUSUM) charting structure is a popular monitoring tool that is often used due to its outstanding ability in recognizing small‐to‐moderate unusual changes in process parameters. The CUSUM control charts have now been widely accepted/applied for monitoring the process dispersion under the assumption that the process does not have outliers. In practice, most of the processes have outliers. Therefore, the use of the interquartile range (IQR) as a plotting statistic is an attractive choice for dispersion monitoring. Hence, in this study, the CUSUM control chart with auxiliary information‐based IQR estimators is proposed and employed to monitor dispersion or variation in the variable LC50 by using quantitative structure–activity relationship (QSAR) aquatic toxicity data set. The proposed IQR‐CUSUM charts are compared to its counterparts by using a variety of main output run‐length functions, such as average run length (ARL), standard deviation of run length (SDRL), and extra quadratic loss (EQL), derived from the distribution of run‐length. Further, results revealed that the proposed CUSUM charts have better detection ability as compared to existing counterpart charts, both in theoretical and practical considerations. Control charts are often used for process monitoring. Cumulative sum (CUSUM) charts are popular monitoring tool. The use of the interquartile range (IQR) as a plotting statistic is an attractive choice for dispersion monitoring. In this study, the CUSUM control chart with auxiliary information‐based IQR estimators is proposed and employed to monitor dispersion or variation in LC50 by using quantitative structure‐activity relationship (QSAR) aquatic toxicity data set. The results revealed that the proposed charts outer performed as compared to existing counterpart charts. [ABSTRACT FROM AUTHOR]

In the statistical process control, the most useful tool to monitor the manufacturing processes in the industries is the control chart. Quality practitioners always desire the charting structure that identifies sustainable changes in the monitoring processes. The sensitivity of the control chart is improved when additional correlated auxiliary information about the study variable is introduced. The regression estimate in the form of auxiliary and supporting variables presents an unbiased and efficient statistic of the mean of the process variable. In this study, auxiliary information‐based moving average (AB‐MA) control chart is designed for efficient monitoring of shifts in the process location parameter. The performance of the AB‐MA control chart is evaluated and compared with existing charts using average run length and other run length characteristics. The comparison reveals that the AB‐MA control chart outperforms the competitors in detecting the small and medium changes in the process location parameter. The application of the proposal is also provided to implement it in real situation. [ABSTRACT FROM AUTHOR]

Memory‐type control charts play a significant role to identify slight changes in the parameters of the production process. In this article, we have proposed a new cumulative sum chart that utilizes the statistic of the homogeneously weighted moving average chart. The performance of the proposed chart is studied using Monte Carlo simulations. The proposed chart is compared with some existing charts under different run length profiles. The run length profile comparisons reveal that the proposed chart performs superior as compared to the existing control charts. A real‐life application using a manufacturing process dataset is also part of this study. [ABSTRACT FROM AUTHOR]

Statistical process control (SPC) plays a vital role in the maintenances and improvements of quality outputs in manufacturing, industrial and service production processes. Control chart is an important SPC tool, used to detect noises and to improve process performance. When the underlying process distribution lacks the assumption of normality, nonparametric (NP) control charts become essential and particularly are useful because their in-control (IC) run length properties remain the same for every continuous distribution. This article develops the NP progressive mean sign (NPPM-SN) chart for monitoring the process target through 100% inspection by taking individual measurements from the process. The performance of the proposed NPPM-SN chart is examined under zero-state and steady-state scenarios. The IC and out-of-control run length properties of the proposed control chart are evaluated using Monte Carlo simulation. The proposed NPPM-SN chart is compared with traditional exponentially weighted moving average (EWMA), nonparametric EWMA sign (NPEWMA-SN) and traditional progressive mean (PM) control charts based on individual measurements using average run length and some other characteristics of the run-length distribution. The proposed NPPM-SN chart is found more robust (for all distributions) and efficient (for skewed distributions) as compared to its competitors. Along with a real-life example related to high voltage power supply, a simulated data example is also presented for the implementation of the proposed NPPM-SN chart. [ABSTRACT FROM AUTHOR]

Recently, a new double progressive mean (DPM) control chart has been proposed in the literature of statistical process control. In the said proposal, an important term is missing in the variance expression of the DPM statistic that affects the detection ability of the proposed chart. In this study, we have derived and provided the correct version of the said variance along with its corresponding control limits. The run length profiles of the DPM chart are investigated, and the results are updated for the new version of the limits. Moreover, a sensitivity analysis between DPM and progressive mean charts based on the different choices of the design parameter is also included in this study. It is revealed that the revised version offers even more efficient outcomes than the previous ones. In addition, a real dataset application is also presented for practical considerations of the refined version. [ABSTRACT FROM AUTHOR]

Control chart is a well‐known tool for monitoring the performance of an ongoing process. The variability of a process is an important parameter that may deteriorate the process performance if it is not taken care on time. In this study, we have proposed some new auxiliary information‐based exponentially weighted moving average (EWMA) charts for improved monitoring of process variability. We employed auxiliary information in some useful forms including ratio, regression, power ratio, ratio exponential, ratio regression, power ratio regression, and ratio exponential regression estimators. The performance of the newly developed charts is evaluated and compared with some existing charts (viz., the NEWMA, the Improved R, the Synthetic R, and the classical R charts), using some useful measures such as average run length (ARL), extra quadratic loss, and relative ARL. The comparative analysis revealed that the proposed charts outperform their counterparts, especially when there is a strong relationship between the study and the auxiliary variables. Finally, an illustrative example is provided for the monitoring of air quality data. [ABSTRACT FROM AUTHOR]

Investigation and removal of unnatural variation in the processes of manufacturing, production and services require application of statistical process control. Control charts are the most famous and commonly used statistical process control tools to trace changes in the manufacturing and nonmanufacturing processes parameter(s). The nonparametric control charts become necessary when the distribution of underlying process is unknown or questionable. The nonparametric charts are robust alternative along with holding property of quick shift detection ability in process parameter(s). In this article, we have proposed nonparametric double exponentially weighted moving average chart based on Wilcoxon signed rank test under simple and ranked set sampling schemes for efficient monitoring of the process location. The proposed control charts are compared with classical exponentially weighted moving average, double exponentially weighted moving average, nonparametric exponentially weighted moving average sign, nonparametric exponentially weighted moving average signed rank, nonparametric cumulative sum signed rank charts using average run length and some other characteristics of run length distribution as performance measures. Comparison reveals that the proposed control charts performs better to detect all kinds of shifts in the process location than existing counterparts. A real-life application related to manufacturing process (the variable of interest is the diameter of piston ring) is also provided for the practical implementation of the proposed chart. [ABSTRACT FROM AUTHOR]

In the service and manufacturing industry, memory‐type control charts are extensively applied for monitoring the production process. These types of charts have the ability to efficiently detect disturbances, especially of smaller amount, in the process mean and/or dispersion. Recently, a new homogeneously weighted moving average (HWMA) chart has been proposed for efficient monitoring of smaller shifts. In this study, we have proposed a new double HWMA (DHWMA) chart to monitor the changes in the process mean. The run length profile of the proposed DHWMA chart is evaluated and compared with some existing control charts. The outcomes reveal that the DHWMA chart shows better performance over its competitor charts. The effect of non‐normality (in terms of robustness) and the estimation of the unknown parameters on the performance of the DHWMA chart are also investigated as a part of this study. Finally, a real‐life industrial application is offered to demonstrate the proposal for practical considerations. [ABSTRACT FROM AUTHOR]

Process control measures are mostly applied in production and manufacturing industries. The most important tool used in these disciplines is control chart. In manufacturing and production processes, when the quality characteristic of interest cannot be directly measured, it becomes essential to apply attribute control charts. To monitor fraction nonconforming of the output, quality practitioners mostly prefer p‐chart. In this article, a new progressive mean (PM) control chart is being proposed for monitoring drift in proportion of nonconforming products. The design evaluations of the proposed chart are made and compared through different properties of run length distribution, such as average run length (ARL), standard deviation of run length (SDRL), and some percentile points. The performance of the proposed chart is assessed under zero‐state and steady‐state scenarios. The proposed PM chart is compared with p‐chart, moving average (MA) chart, optimal CUSUM chart, modified exponentially weighted moving average (EWMA) chart, and runs rules p‐charts for monitoring fraction nonconforming. The proposed chart spots efficiently sustained disturbances in the process as compared with their existing counterparts. Two illustrative examples are also provided; one from real‐life application of nonconforming bearing and seal assemblies data and the other from simulated data for the implementation of PM chart. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The statistical performance of traditional control charts for monitoring the process shifts is doubtful if the underlying process will not follow a normal distribution. So, in this situation, the use of a nonparametric control charts is considered to be an efficient alternative. In this paper, a nonparametric exponentially weighted moving average (EWMA) control chart is developed based on Wilcoxon signed-rank statistic using ranked set sampling. The average run length and some other associated characteristics were used as the performance evaluation of the proposed chart. A major advantage of the proposed nonparametric EWMA signed-rank chart is the robustness of its in-control run length distribution. Moreover, it has been observed that the proposed version of the EWMA signed-rank chart using ranked set sampling shows better detection ability than some of the competing counterparts including EWMA sign chart, EWMA signed-rank chart, and the usual EWMA control chart using simple random sampling scheme. An illustrative example is also provided for practical consideration. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Nonparametric control charts can be useful as an alternative in practice to the data expert when there is a lack of knowledge about the underlying distribution. In this study, a nonparametric cumulative sum (CUSUM) sign control chart for monitoring and detecting possible deviation from the process mean using ranked set sampling is proposed. Ranked set sampling is an effective method when the observations are inexpensive, and measurements are perhaps destructive. The average run length is used as performance measure for the proposed nonparametric CUSUM sign chart. Simulation study shows that the proposed version of the CUSUM sign chart using ranked set sampling generally outperforms than that version of the nonparametric CUSUM sign chart and the parametric CUSUM control chart using simple random sampling scheme. An illustrative example is also provided for practical consideration. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Recently, a homogeneously weighted moving average (HWMA) chart has been suggested for the efficient detection of small shifts in the process mean. In this study, we have proposed a new one-sided HWMA chart to effectively detect small changes in the process dispersion. The run-length (RL) profiles like the average RL, the standard deviation RL, and the median RL are used as the performance measures. The RL profile comparisons indicate that the proposed chart has a better performance than its existing counterpart's charts for detecting small shifts in the process dispersion. An application related to the Dhahran wind farm data is also part of this study. [ABSTRACT FROM AUTHOR]