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In this paper, an attempt is made to develop a new variable control chart under the resubmitted sampling scheme. The resubmission of a sample allows retaking a sample to pass through inspection/analyzing if there is a question on the inspection of the sample at any stage. The proposed control chart includes the X-bar chart as a special case. The performance of the proposed control chart is assessed by determining the in-control and out-of-control average run lengths for the various parametric settings. For illustrative purposes, a real-life example is presented to explain a detailed implementation of the proposed control chart. [ABSTRACT FROM AUTHOR]

Inspection and control of products' quality is a need of the day using effective and emerging methods of technology. In many cases of well-organized production processes, only the extreme values of key-processing variables are meaningful to inspect and analyze. In this article, a control chart for largest extreme observations has been proposed using Frechet distribution based on weighted variance method. The proposed chart has been compared with existing control charts available in this scenario through average run length and percentage decrease in average run length. Further, proposed chart has been applied to the data of time to failure in microcircuits describe the commercial applicability of the study. The proposed chart has dominated the existing charts in identifying process deterioration. [ABSTRACT FROM AUTHOR]

Statistical process control is extensively applied for monitoring the production process. One of the important tools of statistical process control for detecting the unusual changes in the manufacturing process is the control chart. In the use of a control chart, samples or subgroups are selected in a manner that if unusual changes are present, the chance for differences between subgroups will be maximized, while the chance for differences within a subgroup will be minimized. However, there are different practical engineering processes where this assumption would not be applicable. In this paper, we considered a Wind Energy process where differences within a subgroup are significant and proposed a new control chart namely Ladder Ascending Control Chart under this situation. The Ladder Ascending Control Chart is based on order statistics by using order statistic distribution as the underlying distribution. Shewhart type control limits formulae for the order statistic mean have been built. It is applied to wind energy for monitoring the wind speed using the Weibull distribution as the underlying distribution of wind speed. The average run length is provided as a performance measure for the Ladder Ascending Control Chart. A real-life example is provided for its application and interpretation. [ABSTRACT FROM AUTHOR]

In this manuscript, we construct an attribute control chart (ACC) for the number of defective items using time-truncated life tests (TTLT) when the lifetime of a manufacturing item follows two lifetime data distributions: the half-normal distribution (HND) and the half-exponential power distribution (HEPD). To assess the potential of the proposed charts, necessary derivations are made to obtain the value of the average run length (ARL) when the production process is in-control and out-of-control. The performance of the presented charts is evaluated for different sample sizes, control coefficients, and truncated constants for shifted phases in terms of ARL. The behavior of ARLs is studied for the shifted process by introducing shifts in its parameters. The advantages of the proposed HEPD-based chart are discussed in the form of ARLs with HND and Exponential Distribution (ED) based ACCs under TTLT, showing the excellent assessment of the proposed chart. Additionally, the advantages of another proposed ACC using HND are compared with ED-based ACC, and the findings support the HND in the form of smaller ARLs. Finally, simulation testing and real-life implementation are also discussed for functional purposes. [ABSTRACT FROM AUTHOR]

The control chart is the most valuable tool in the manufacturing process to track the output process in the industries. Quality specialists always want a visual framework that recognizes sustainable improvements in the monitoring processes. The efficiency of a control chart is increased by utilizing a memory-based estimator or by using any extra information relevant to the key variable. In this study, we present Extended EWMA (EEWMA) and EWMA based monitoring charts for observing the process location using moving average (MA) statistic under two different situations, i.e., when some extra information is known and unknown. We also propose an EEWMA control chart using Auxiliary Information. The output of these charts is evaluated and contrasted to the various existing charts on the basis of average run length (ARL). The comparison indicates that the proposed charts outperform rivals in identifying all types of shifts in the process location parameter. The implementation of these plans is also rendered to incorporate them in a practical situation. [ABSTRACT FROM AUTHOR]

In this paper, we propose a mixed control chart to monitor the process quality using attribute data combined with variable data. The proposed control chart proceeds like an np control chart based on the number of nonconforming parts but requires variable data only when the decision is indeterminate. The control coefficients are determined by considering the in-control and the out-of-control average run lengths for various specified parameters. The extensive tables are provided for the industrial use. The advantages of the proposed control chart are discussed over the traditional np control chart. [ABSTRACT FROM AUTHOR]

Modern and emerging techniques of technology have brought a revolution in quality inspection of products. When events in highly efficient production processes occur rarely, it requires to inspect and monitor the time between occurrence of these events (TBE). The exponential and gamma distributions are commonly used models for time between events (TBE) data. In this article, a new monitoring scheme has been established for TBE data based on exponential and gamma distributions. In a previous research, transformation‐based control charts have been developed for TBE. The proposed study is aimed to use the exact probability distribution of charting statistic rather than applying transformations to data and this has remained still unaddressed. Average run length (ARL) and percentage decrease in ARL (ΔARL) have been calculated using Monte Carlo simulations and the proposed monitoring method has been compared with existing techniques applied to transformed data. The proposed scheme provides a simpler design structure and better performance on different sample sizes in identifying annoying process variations. Further, the technique has been applied to simulated and real‐life data sets of time between manufacturing plant accidents to highlight the worth and particle applicability of the proposed work. [ABSTRACT FROM AUTHOR]

In this paper, we propose extended exponential weighted moving average (EEWMA) control chart (CC) for the surveillance the process mean using repetitive sampling scheme under the assumption that quality characteristic of a product is a quantitative variable following the normal distribution. Two control limits named as inner and outer control limits of the proposed CC are derived using the in-control process mean and variance. The necessary measures are derived to obtain the process in-control and out-of-control average run length (ARL) and tabulated for different process shifts and smoothing constants. Comparisons are made to check the capability of intended CC with the EEWMA CC and repetitive EWMA CC in term of ARL. It has been observed that the presented CC shows better results in term of early detection of divergence in process mean. A simulation study is carried out to illustrate the performance of the proposed CC. An industrial example has been incorporated for its practical purpose. [ABSTRACT FROM AUTHOR]

In this paper, we present a repetitive sampling method to construct control charts using exponentially weighted moving averages (EWMA) and double exponentially weighted moving averages (DEWMA) to monitor shift in the process. For non-normal processes, t-distribution with various degrees of freedom (i.e. df = 4 , 10 , 20 , 40 , 50) is used as symmetric distribution, gamma distribution with unit scale parameter and various shape parameters (i.e. 0.5 , 1 , 2 , 3 , 4) is used as positively skewed distribution and Weibull distribution with unit scale parameter and various shape parameters (i.e. 10 and 20) is used as negatively skewed distribution. We use Monte Carlo simulations to check whether the process is out of control. We use average run length as a tool to find the ability of proposed control charts to identify a shift earlier in a process, as compared to other control charts currently used to monitor the same type of process. The proposed control charts are applied to two real datasets. [ABSTRACT FROM AUTHOR]

In this paper, we presented a memory type control chart (CC) based on multiple dependent state (MDS) sampling to pinpoint the slight variation in the process mean for the quality trait of normal distribution (ND). Two pairs of control limits denominated as internal and external control limits are derived using under control mean and variance. The essential steps are taken to get the value of average run length (ARL) for stable and disturb process. Various tables of ARLs are erected using different smoothing constants, shifts and MDS parameter. Comparisons are established to assess the effectiveness of initiated CC with the various existing CC in term of ARL. It has been ascertained that offered CC manifest the best performance in searching out the diminutive changes in the process mean. Two examples, one is based on simulation study and other is related to real-life data, have been discussed for its practical purpose. [ABSTRACT FROM AUTHOR]

Control charts are developed to make the specific quality measures for a successful production process and follow normal distribution behaviors. But some real‐life practices do not match such practices and exhibit some positively skewed behavior like lognormal distribution. The present study has considered this situation and proposed a monitoring control chart based on lognormal process variation using a repetitive sampling scheme. This concept proved better for detecting shifts as quickly as possible, and compared with the existing concept, results are elaborated through extensive tables. The average run lengths and standard deviations of the run lengths are being used as a performance evaluation measures and computed by using Monte Carlo simulations performed in R language. A real‐life situation has been discussed in the example section to strengthen the proposed control chart concept in a real‐life situation. [ABSTRACT FROM AUTHOR]

An attribute np control chart has been designed using resampling systems for monitoring non- -conforming items under exponentiated half-logistic distribution. We suppose that lifetime follows exponentiated half-logistic distribution. For the proposed control charts, the optimal parameters and control limits have been obtained. The operational formulas for in-control and out of control average run lengths (ARLs) have been 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 is evaluated with several existing charts with regard to ARLs, which empower the presented chart and prove far better for timely detection of assignable causes. A wide range of tables, a real-life example, and simulation study for RGS and MDS are given for a better understanding of the problem. [ABSTRACT FROM AUTHOR]

In this article, a new non parametric control chart based on the modified or controlled exponentially weighted moving average (EWMA) statistic is developed to monitor the process deviation from the target value. The proposed control chart is evaluated for different values of design parameters using the average run length as a performance criterion under various sample sizes. The proposed chart is compared with the existing non parametric EWMA sign control chart. It is observed that the proposed chart is better than the existing EWMA sign control chart in terms of run length characteristics. An empirical example is provided for the practical implementation of the proposed chart. [ABSTRACT FROM AUTHOR]

In this paper, successive sampling (SS) mean monitoring control chart is proposed for two successive occasions using multiple dependent state (MDS) sampling and characterized as MDS‐SS control chart. The sample size is selected on the basis of SS for the control statistic in two pairs of the control limits. The average run length is evaluated, and the performance of the proposed concept over the existing SS control chart is presented using a real‐life example. The SS monitoring chart concept is compiled with MDS, and it becomes more sophisticated in detection of warning signs. [ABSTRACT FROM AUTHOR]

Repetitive sampling has been found very popular in improving the control chart techniques for last couple of years. In repetitive sampling based control charts, there are two additional control limits inside the usual upper control limit (UCL) and lower control limit (LCL). If any subgroup crosses these limits but remain inside outer limits, it is deferred and replaced with another selection. Process is said to be out of control if any subgroup falls outside UCL/LCL. In this article, the technique has been modified by introducing a relation between outer and inner control limits in terms of a ratio and need of this modification has also been justified by highlighting a gap in the existing technique. By using Monte Carlo simulation, several results have been generated relevant to different sample sizes and introduced ratios. The results have been described with the help of average run length (ARL) tables that how the efficiency of control chart is effected by using different ratios. The modification in the technique also provides variety of alternatives within the scope of repetitive based control charts. All the discussed options have summarized to one table to see that how the control limits under this technique behave and impact on detecting shifts in the process average. The schemes have been interpreted in the light of above ratio and their comparison has been described under different sample sizes that facilitate the user to select most appropriate scheme for a desired process control. An example has been included by choosing one of the proposed schemes to show the application and performance of the proposed control chart in a manufacturing process. [ABSTRACT FROM AUTHOR]

A control chart based on double exponentially weighted moving average and Benjamini‐Hochberg multiple testing procedure is proposed that controls the false discovery rate (FDR). The proposed control chart is based on probabilities (or P values) to accept or reject the null hypothesis of the underlying process is in control. To make a decision, instead of using only the current probability, previous "m" probabilities are considered. The performance of the control chart is evaluated in terms of average run length (ARL) using Monte Carlo simulations. Procedure for estimation of parameters used in the control chart is also discussed. The proposed control chart is compared with previous control charts and found to be more efficient in controlling the false discovery rate. [ABSTRACT FROM AUTHOR]

The Weibull distribution is one of the most popular distributions for lifetime modeling. However, there has not been much research on control charts for a Weibull distribution. Shewhart control is known to be inefficient to detect a small shift in the process, while exponentially weighted moving average (EWMA) and cumulative sum control chart (CUSUM) charts have the ability to detect small changes in the process. To enhance the performance of a control chart for a Weibull distribution, we introduce a new control chart based on hybrid EWMA and CUSUM statistic, called the HEWMA-CUSUM chart. The performance of the proposed chart is compared with the existing chart in terms of the average run length (ARL). The proposed chart is found to be more sensitive than the existing chart in ARL. A simulation study is provided for illustration purposes. A real data is also applied to the proposed chart for practical use. [ABSTRACT FROM AUTHOR]

In this manuscript, a control chart is designed for two-piece normal distribution using repetitive sampling. The necessary measures to determine the average run lengths for in control and out-of-control process are given. The average run lengths are presented for various specified parameters and shift constants. The efficiency of the proposed chart is compared with the existing control chart using single sampling. The application of the proposed chart is given with the help of an example. [ABSTRACT FROM AUTHOR]

In practice, the products can be manufactured through several stages. In this manuscript, we will propose an attribute control chart plotting the number of defectives for a two-stage process. The in-control average run length is derived and the out-of-control average run lengths are also analyzed according to the process shifts in the first and/or the second stage process. The tables of the average run lengths are given for various specified parameters. An example is given with synthetic data for the illustration of the proposed control chart. [ABSTRACT FROM AUTHOR]

The control charts are designed to examine and detect the changes in the manufacturing process. We present repetitive exponentially weighted moving average (EWMA) control chart using regression estimator for monitoring process mean based on ranked set sampling (RSS). We study the detection ability of the proposed control chart for monitoring process mean when mean is shifted due to some random causes. The performance and efficiency of proposed repetitive EWMA-RSS control chart is evaluated in terms of ARLs for different process shift. The effectiveness of the repetitive EWMA-RSS control chart is compared with the single EWMA-RSS control chart. The practical application of the proposed chart is also discussed for better understanding. [ABSTRACT FROM AUTHOR]

This article proposes a new control chart to monitor the process mean based on the sample statistics from two successive occasions. First occasion units are independently drawn to obtain information of the study variable, and the second occasion units are drawn collectively with a retained portion of first occasion as matched units, while the other portion is the collection of the fresh units from the remaining unselected units at first occasion. The in-control and out-of-control average run lengths (ARLs) are derived for performance assessment under mean shifts. The performance of the proposed control chart is compared with traditional Shewhart control chart and a EWMA chart in terms of ARLs, which exhibits better performance when process is shifted from the target mean. [ABSTRACT FROM AUTHOR]

In this article a criteria have been defined to classify the existing repetitive sampling into soft, moderate and strict conditions. Behind this division a ratio has been suggested i.e. c2 (constant used in repetitive limits) to c1 (constant used in control limit) in slabs. A restricted criterion has been devised on the existing repetitive sampling. By embedding the proposed schematic in the control chart it becomes highly efficient in detecting the shifts quite earlier as well as it detects even smaller shifts at smaller ARLs. To facilitate the user for best choice the restricted criterion has further categorized to softly restricted, moderately restricted and strictly restricted. The restricted conditions are dependent on value of restriction parameter 'm' varies 2 to 6. The application of proposed scheme on selected cases is given in tables which are self-explanatory. [ABSTRACT FROM AUTHOR]

In this paper, we present a new control chart for exponentially distributed quality characteristics using variable transformation and exponentially weighted moving average (EWMA). The in-control and out-of-control average run lengths (ARLs) are derived using the normal approximation. The parameters of the proposed control chart are determined by considering the target in-control ARL. The efficiency of the proposed control chart is discussed in terms of ARLs over the existing control chart without using EWMA. The application of the proposed control chart is discussed with the help of a real example. [ABSTRACT FROM AUTHOR]

In this paper, a new nonparametric control chart based on the exponentially weighted moving average (EWMA) sign statistic is proposed using repetitive sampling. The control chart is proposed to effectively detect the process mean shift away from the target value without the distributional assumption on the quality characteristic. The proposed control chart is based on two pairs of upper and lower control limits having different control coefficients. The in-control and the out-of-control average run lengths of the proposed control chart are evaluated through the Monte Carlo simulation. The proposed control chart is shown to be more efficient than the existing EWMA sign control chart in terms of the average run length. [ABSTRACT FROM AUTHOR]

Among the Statistical Process Control (SPC) techniques, control charts are considered to be high weight-age due to their effectiveness in process variation. As the Shewhart's charts are not that active in monitoring small and moderate process variations, the statisticians have been making efforts to improve the performance of the control chart by introducing several techniques within the tool. These techniques consist of experimenting with different estimators, different sampling selection techniques, and mixed methodologies. The proposed chart is one of the examples of a mixed chart technique that has shown its efficiency in monitoring small variations better than any of the existing techniques in the specific situation of auxiliary information. To show and compare its performance, average run length (ARL) tables and ARL curves have been presented in the article. An industrial example has also been included to show the practical application of the proposed chart in a real scenario. [ABSTRACT FROM AUTHOR]