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"Control charts"
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The checklist manifesto : how to get things right
Reveals the surprising power of the ordinary checklist now being used in medicine, aviation, the armed services, homeland security, investment banking, skyscraper construction, and businesses of all kinds.
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Variable parameters memory-type control charts for simultaneous monitoring of the mean and variability of multivariate multiple linear regression profiles
Variable parameters (VP) schemes are the most effective adaptive schemes in increasing control charts' sensitivity to detect small to moderate shift sizes. In this paper, we develop four VP adaptive memory-type control charts to monitor multivariate multiple linear regression profiles. All the proposed control charts are single-chart (single-statistic) control charts, two use a Max operator and two use an SS (squared sum) operator to create the final statistic. Moreover, two of the charts monitor the regression parameters, and the other two monitor the residuals. After developing the VP control charts, we developed a computer algorithm with which the charts' time-to-signal and run-length-based performances can be measured. Then, we perform extensive numerical analysis and simulation studies to evaluate the charts’ performance and the result shows significant improvements by using the VP schemes. Finally, we use real data from the national quality register for stroke care in Sweden, Riksstroke, to illustrate how the proposed control charts can be implemented in practice.
Journal Article
On the performance of CUSUM control charts for monitoring the coefficient of variation with measurement errors
In this paper, we investigate the effect of the measurement error on the performance of the cumulative sum (CUSUM) control charts monitoring the coefficient of variation. The measurement errors are supposed to follow a linear covariate error model. The obtained results show that the precision error ratio and the accuracy error have negative impact on the chart performance. Moreover, in order to overcome the difficulty in predetermining a specific value for the process shift size, we suggest to optimize the parameters of the charts according to the random shift size in a given interval. The robustness of the proposed method is studied. An example is given to illustrate the use of the CUSUM charts on a real quality control problem from sintering process.
Journal Article
Max-mixed EWMA control chart for joint monitoring of mean and variance: an application to yogurt packing process
The Max-Mixed EWMA Exponentially Weighted Moving Average (MM EWMA) control chart is a statistical process control technique used for joint monitoring of the mean and variance of a process. This control chart is designed to detect small and moderate shifts in the mean and variance of a process by comparing the maximum of two statistics, one based on the mean and the other on the variance. In this paper, we propose a new MM EWMA control chart. The proposed chart is compared with existing control charts using simulation studies, and the results show that the chart performs better in detecting small and moderate shifts in both the mean and variance. The proposed chart can be helpful in quality control applications, where joint monitoring of mean and variance is necessary to ensure a product’s or process’s quality. The real-life application of the proposed control chart on yogurt packing in a cup data set shows the outperformance of the MM EWMA control chart. Both simulations & real-life application results demonstrate the better performance of the proposed chart in detecting smaller shifts during the production process.
Journal Article
Average Run Length in TEWMA Control Charts: Analytical Solutions for AR(p) Processes and Real Data Applications
This study aims to examine the explicit solution for calculating the Average Run Length (ARL) on the triple exponentially weighted moving average (TEWMA) control chart applied to autoregressive model (AR(p)), where AR(p) is an autoregressive model of order p, representing a time series with dependencies on its p previous values. Additionally, the study evaluates the accuracy of both explicit and numerical integral equation (NIE) solutions for AR(p) using the TEWMA control chart, focusing on the absolute percentage relative error. The results indicate that the explicit and approximate solutions are in close agreement. Furthermore, the study investigates the performance of exponentially weighted moving average (EWMA) and TEWMA control charts in detecting changes in the process, using the relative mean index (RMI) as a measure. The findings demonstrate that the TEWMA control chart outperforms the EWMA control chart in detecting process changes, especially when the value of λ is sufficiently large. In addition, an analysis using historical data from the SET index between January 2024 and May 2024 and historical data of global annual plastic production, the results of both data sets also emphasize the superior performance of the TEWMA control chart.
Journal Article
Control theory for skewed distribution under operation side of the telecommunication industry and hard-bake process in the semiconductor manufacturing process
Statistical process control basically involves inspecting a random sample of the output from a process and deciding whether the process is producing products with characteristics that fall within a predetermined range. It is used extensively in the field of reliability engineering. The reliability of the production process is thoroughly monitored for any internal variation using the SPC. The aim is always to settle such variations through a proper control monitoring. If the underlying distribution of the process is known to the researcher than the use of parametric control charts are useful but in many cases when there is doubt about the distribution of the process then it is preferred to use non parametric control charts. In this paper we propose the modified Exponentially weighted moving average (EWMA), Double Exponentially weighted moving average (DEWMA), Hybrid Exponentially weighted moving average (HEWMA), Extended Exponentially weighted moving average (EEWMA), Modified Exponentially weighted moving average (MEWMA) and mix- type control charts by mixing these control charts with Tukey control chart EWMA-TCC, DEWMA-TCC, HEWMA-TCC, EEWMA-TCC, MEWMA-TCC for the shape parameter of the Kumaraswamy Lehmann-2 Power function distribution (KL2PFD).
Journal Article
Machine learning based parameter-free adaptive EWMA control chart to monitor process dispersion
Conventional control charts track changes in the process by using predefined process parameters. Conversely, during online monitoring, adaptive control charts modify the process parameters. To improve the process dispersion monitoring in various operational environments, this study presents an adaptive exponentially weighted moving average (AEWMA) control chart based on support vector regression (SVR). This study investigates the efficacy of different kernels such as linear, polynomial, and radial basis functions (RBF) within the SVR framework. By adapting the smoothing constant to the shift’s size in process dispersion, the suggested SVR-based AEWMA control chart makes better use of the strengths of the RBF kernel to identify shifts in the process dispersion. To demonstrate the method’s effectiveness, real-life data is used in a practical application, highlighting the adaptability and reliability of the SVR-based AEWMA control chart for monitoring process dispersion. The code and supplementary data set file may be found at (
https://github.com/muhammadwaqaskazmi/ARL-SDRL-Codes
).
Journal Article
Comparing statistical process control charts for fault detection in wastewater treatment
Fault detection is an important part of process supervision, especially in processes where there are strict requirements on the process outputs like in wastewater treatment. Statistical control charts such as Shewhart charts, cumulative sum (CUSUM) charts, and exponentially weighted moving average (EWMA) charts are common univariate fault detection methods. These methods have different strengths and weaknesses that are dependent on the characteristics of the fault. To account for this the methods in their base forms were tested with drift and bias sensor faults of different sizes to determine the overall performance of each method. Additionally, the faults were detected using two different sensors in the system to see how the presence of active process control influenced fault detectability. The EWMA method performed best for both fault types, specifically the drift faults, with a low false alarm rate and good detection time in comparison to the other methods. It was shown that decreasing the detection time can effectively reduce excess energy consumption caused by sensor faults. Additionally, it was shown that monitoring a manipulated variable has advantages over monitoring a controlled variable as set-point tracking hides faults on controlled variables; lower missed detection rates are observed using manipulated variables.
Journal Article
Design of an EWMA control chart by adaptation of smoothing constant based on a function of estimated shift
This study introduces a novel Adaptive EWMA (AEWMA) control chart designed to monitor the mean of a normally distributed process with enhanced responsiveness. The proposed methodology dynamically adjusts the smoothing constant based on a proposed continuous function of the estimated mean shift derived from the EWMA statistic. The Monte Carlo simulations are conducted to assess the performance of the AEWMA chart across various magnitudes of process mean shifts, using run-length profiles as the primary evaluation metric. The results indicate that the AEWMA chart outperforms traditional methods in terms of detection efficiency. To demonstrate its practical applicability, the AEWMA chart is applied to a real-world manufacturing dataset, specifically analyzing the flow width resistance of substrates. The findings highlight the efficiency of the proposed chart, making it a valuable tool for improving process monitoring and quality control in industrial environments.
Journal Article
Variable sample size based EWMA control chart with an exponential scaling mechanism for production process monitoring
Statistical Process Control is essential for ensuring process stability and detecting variations in a production environment. This study introduces a control chart based on the Exponentially Weighted Moving Average (EWMA) that uses an adaptive sample size. The proposed approach enhances shift detection by dynamically adjusting the sample size in response to changes in process variation. Extensive Monte Carlo simulations were performed to assess the performance of the proposed control chart, focusing on metrics such as the Average Run Length (ARL) and the Standard Deviation of Run Length (SDRL). The findings show that the new chart surpasses both the Fixed Sample Size EWMA (FEWMA) and the Variable Sample Size EWMA charts, particularly in detecting small to moderate shifts in the process. This approach strikes a balance between detection sensitivity and computational efficiency, enabling prompt identification of process changes while maintaining robustness during in-control conditions. To illustrate its practical applicability, a real-world dataset was analyzed, demonstrating the effectiveness of the proposed method in actual process monitoring scenarios.
Journal Article