Optimal Processes Control for a Failure Mechanism

The implementation of Statistical Quality Control (SQC) has been tracked in various areas, such as agriculture, environment, industry, and health services. The employment of SQC methodologies is frequently employed for monitoring and identification of process irregularities across various fields. This research proposes and implements a novel SQC methodology in agricultural areas. A control chart is one of the SQC tools that facilitates real-time monitoring of multiple activities, including agricultural yield, industrial yield, and hospital outcomes. Advanced control charts with symmetrical data are being subjected to the new SQC method, which is suitable for this purpose. This research aims to develop a novel hybrid exponentially weighted moving average control chart for detecting the coefficient of variation (CV) using a repetitive sampling method called the HEWMARS-CV control chart. It is an effective tool for monitoring the mean and variance of a process simultaneously. The HEWMARS-CV control chart used the repetitive sampling scheme to generate two pairs of control limits to enhance the performance of the control chart. The proposed control chart is compared with the classical HEWMA and Shewhart control charts regarding the average run length (ARL) when the data has a normal distribution. The Monte Carlo simulation method is utilized to approximate the ARL values of the proposed control charts to determine their performance. The proposed control chart detects small shifts in CV values more effectively than the existing control chart. An illustrative application related to monitor the wheat yield at Rothamsted Experimental Station in Great Britain is also incorporated to demonstrate the efficiency of the proposed control chart. The efficiency of the proposed HEWMARS-CV control chart on the real data shows that the proposed control chart can detect a shift in the CV of the process, and it is superior to the existing control chart in terms of the average run length.

In this paper, the moving average control chart is developed under time truncated test to monitor the number of failures when the lifetime of an item follows the Rayleigh and inverse Rayleigh distributions. The number of failures is detected when a planned number of items has been placed on a time truncated testing experiment. The average run lengths (ARL) for various parameter settings are used to evaluate the performance of the proposed control chart. Also, the control chart parameters such as test time multiplier () and chart coefficient (k) are obtained by contemplating the in‐control ARL for various sample sizes. The study revealed that the proposed control chart outperform the existing time truncated attribute control chart in the quick detection of shifts in process parameters. The application of the proposed chart methodology is also demonstrated using simulated data.

Enhancing the ability of control charts for detecting shifts of small magnitude in the process parameter is desirable. In this paper, a double moving average control chart based on the sample standard deviation is proposed for detecting shifts in the process variability. The performance of the proposed control chart is investigated in terms of average run length and compared with existing control charts. The results show that the proposed control chart performs better than the existing control charts. A numerical example based on simulated and real-life data is provided to demonstrate the application of the proposed chart.

The memory control charts, including cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts, are widely used to monitor small‐to‐moderate shifts in the process location and/or dispersion. The homogenously weighted moving average (HWMA) control chart is the advanced form of the EWMA control chart to monitor process location shifts. Besides, the auxiliary information‐based memory control charts efficiently monitor process location shifts. The objective of this study is to propose an auxiliary information based double HWMA, symbolized as control chart to further enhance the monitoring of process location shifts. The control chart is modeled by mixing the auxiliary information‐based HWMA plotting statistic features into the other HWMA control chart. For numerical results, Monte Carlo simulations are used as a computational technique. Famous performance measures including average run length (), extra quadratic loss, relative , and performance comparison index are used to compare the performance of proposed control chart against other control charts including classical CUSUM and EWMA, mixed ‐, auxiliary information based EWMA (), HWMA, auxiliary information based HWMA (), and double HWMA control charts. The comparisons revealed that the proposed control chart outperformed other control charts, especially for small‐to‐moderate process location shifts. An automobile braking system application is also provided for users and practitioners to demonstrate the importance of the proposed study from a practical perspective.

A new exponentially weighted moving average sign chart using repetitive sampling

This paper proposes a new np control chart by utilizing the information on the past subgroups through the idea of multiple dependent state sampling. The proposed control chart has two pairs of control limits and their parameters are determined by considering the target in-control average run length (ARL). To check the efficiency of the proposed control chart, the out-of-control ARLs are evaluated for different process shift. The efficiency of the proposed chart is compared with the traditional Shewhart np control chart and the results show that our proposed control chart has a better performance than the traditional np control chart.

The control charts are essential tools in order to monitor the process quality as well as are used in numerous industries. The classical Shewhart S‐control chart is the most prevalent control chart in Statistical Process Control (SPC) for assessing changes in process over time. This paper modifies the S‐control chart by proposing SDDM‐control chart based on the SDDM (decile mean standard deviation), which presume that the quality function is monitored normally. The traditional and proposed control charts’ performances under study are evaluated via a Monte‐Carlo simulation study by estimating expected widths (EW) as well as expected out‐of‐control points (EPO). In the simulation, the average out of control run length (ARL1) is considered the best method for analyzing sensitivity of the control charts. The outcomes of the simulation analysis revealed that the proposed SDDM‐control chart is well performed and very close contender of the classical S‐control chart insofar that the improvement in process variability is very similar to that of the S‐control chart. In addition, the architecture layout of the proposed SDDM‐control chart is more powerful than the conventional S‐control chart for the smaller sample size value (n) that is the most realistic situation in SPC applications. Hence the proposed chart is equally compatible for shift detection under normal process. In case of abnormal processes, the proposed SDDM‐control limits are less affected by the presence of outliers. A real‐life dataset is evaluated to help our observations from the simulation analysis for illustrative purposes. Consequently, the SDDM version of the S‐control chart is recommended to be used by the practitioners in various fields of industry, engineering, medical and physical sciences.

The zero-inflated Poisson (ZIP) model is an extension of the standard Poisson distribution. It is often used to describe a near zero-defect process with occasional occurrences of non-conforming products. In the past, research on the control charts for ZIP process has concentrated on univariate ZIP process where there is only one type of defect. However, it is common in some high quality processes that there are several types of defects to be considered and the count variables are correlated. It is not appropriate to monitor the process using independent univariate ZIP based control charts. In this paper, a control charting procedure using a combination of two cumulative sum charts is proposed for monitoring shifts in a bivariate ZIP (BZIP) process, which is a special case of the multivariate ZIP model. We use simulations to obtain the upper control limit of the control charts based on a specified in-control average number of observations to signal. We also use simulations to evaluate the control charting procedure in three situations: shifts only in the p-set parameters; shifts only in the λ-set parameters; and shifts in all the parameters. The simulation results show that the proposed control charts are effective in detecting shifts in the parameters of a BZIP process. Finally, we present an application of our proposed method in the light emitting diode packaging industry.

In the present paper, we propose a control chart based on extended exponentially weighted moving average (EEWMA) statistic to detect a quick shift in the mean. The mean and variance expression of the proposed EEWMA statistic are derived. The proposed EEWMA statistic is unbiased and simulation results show a smaller variance as compared to the traditional EWMA. The performance of the proposed control chart with the existing chart based on the EWMA statistic is evaluated in terms of average run length (ARL). Various tables were constructed for different values of parameters. The comparison of the EEWMA control chart with the traditional EWMA and Shewhart control charts illustrates that the proposed control chart performs better in terms of quick detection of the shift. The working procedure of the proposed control chart was also illustrated by simulated and application data.

This study considers the construction of control charts when outliers could occur in the data. Simply increasing the width of control limits will make it more difficult to detect an out-of-control signal. This study aims to determine weights for each observation by measuring their respective distances to the average. The study proposes a retrospective weighted average and weighted inter-quartile range (WIQR) control chart based on the normal distribution that accounts for the false alarm rate, special causes, outliers, and a combination of special causes and outliers that might exist within the process. In addition to validating the detection effectiveness of the proposed control charts, this study also compares various average-type and range-type control charts for detecting the outliers and special causes. The proposed control charts show superior detection effectiveness in four tested scenarios compared to other types of control charts. Finally, the study also illustrates the practical application of the control charts for measuring the tensile strength of a deformed bar in coil. [Received 10 February 2014; Revised 21 August 2015; Revised 3 November 2015; Accepted 14 December 2015]

Control charts are widely used to monitor manufacturing processes for a deterioration in stability. We propose repetitive group sampling control charts based on the process capability index Cpk for monitoring process average when the quality characteristic follows a normal distribution. The performance of the repetitive Cpk control chart is reported and compared with the existing control chart in terms of the average run length. It is found that the proposed control chart is effective for quickly detecting small shifts in the process mean.

Designing of Run Rules t control charts for monitoring changes in the process mean

Nonparametric control charts are among the most important tools of statistical process control. Such charts are useful when there is a lack of knowledge about an underlying distribution or its parameters. Most existing nonparametric control charts are used for monitoring location parameters: they may perform poorly when the scale parameters change arbitrarily over time. In this paper, we propose a new nonparametric control chart based on a powerful likelihood ratio test and the exponential weighted moving-average (EWMA) control chart. Its nonparametric property is applied via a consistent series density estimator in the exponential family. The proposed control chart does not require historical reference samples and can be monitored by fixed control limits. The Monte Carlo simulation results show that the proposed control chart performs well in monitoring both mean and variance shifts, especially in monitoring variance or large mean shifts. Furthermore, a real-data example is given to illustrate the effectiveness of the proposed method.

A control chart is one of the primary techniques used in statistical process control. In phase I, historical observations are analysed in order to construct a control chart with which to determine whether the process has been in control over the period of time in which the data were collected. The presence of multiple outliers may go undetected by the usual control charts, such as Hotelling's T2 due to the masking effect. In this paper we propose a robust alternative to Hotelling's T2 control chart with estimators defined using trimming. Simulation studies show that the proposed control chart is more effective than T2 in detecting outliers. Copyright © 2008 John Wiley & Sons, Ltd.

ABSTRACTThe study proposes a weighted bootstrap control chart based on the strata and determines suitable weights for each stratum by measuring the relative error of estimated Type I risks (Err). In addition to validating the detection effectiveness of the proposed control chart, this study also compares several types of control charts for detecting the Type I, Type II risks, and median run lengths. The proposed control chart shows superior detection effectiveness in six skewed distributions compared to other types of control charts.