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There are certain areas of science and technology, such as agriculture, ecology, and environmental studies, that emphasize designing competent sampling strategies. The ranked set schemes, particularly the neoteric ranked set sampling (NRSS), are one method that meets such objectives. The NRSS provides plans that incorporates expert knowledge while choosing samples, which is beneficial. This study proposes a novel scheme for creating dispersion charts based on NRSS. The proposed scheme aims to improve the accuracy of dispersion charts by reducing the impact of outliers and non-normality in data sets. As a highly effective method in estimating population parameters, NRSS is used to select samples from the data set. The proposed dispersion charts are assessed based on individual performance measure criteria at shifts of different magnitudes. The dispersion charts created using this new scheme are compared with traditional dispersion charts, and the results demonstrate that the proposed scheme produces charts with higher accuracy and robustness. The study highlights the potential benefits of using NRSS-based dispersion charts in various fields, including quality control, environmental monitoring, and process control. An actual data application from a non-isothermal continuous stirred tank chemical reactor model further validates the simulation results. [ABSTRACT FROM AUTHOR]

The special causes of variations, which is also known as a shift, can occur in a single or more than one related process characteristics. Statistical process control tools such as control charts are useful to monitor shifts in the process parameters (location and/or dispersion). In real-life situation, the shift is emerging in different sizes, and it is hard to identify it with classical control charts. Moreover, more than one process of characteristics required special attention because they must monitor jointly due to the association among them. This study offers two adaptive control charts to monitor the different sizes of a shift in the process mean vector. The novelty behind this study is to use dimensionally reduction techniques such as principal component analysis (PCA) and an adaptive method such as Huber and Bi-square functions. In brief, the multivariate cumulative sum control chart based on PCA is designed, and its plotting statistic is utilized as an input in the classical exponentially weighted moving average (EWMA) control chart. The run length (RL) properties of the proposed and other control charts are obtained by designing algorithms in MATLAB through a Monte Carlo simulation. For a single shift, the performance of the control charts is assessed through an average of RL, standard deviation of RL, and standard error of RL. Likewise, overall performance measures such as extra quadratic loss, relative ARL, and the performance comparison index are also used. The comparison reveals the superiority over other control charts. Furthermore, to emphasize the application process and benefits of the proposed control charts, a real-life example of the wind turbine process is included. [ABSTRACT FROM AUTHOR]

Linear profiles are quite popular in establishing relationships among different variables associated with each other in an ongoing process. Control charting methodologies for these linear profiles are used to monitor and improve the performance of a process. The commonly used Phase I methodologies of linear profiles are mostly based on simple random selection procedures. In this study, we intend to improve the existing Phase I profile methods by considering different ranked set strategies including ranked set sampling (RSS), median RSS (MRSS) and extreme RSS (ERSS). The profile monitoring is considered in terms of three main parameters namely slope, intercept and error variance for efficient detection of any assignable cause(s). We have used probability to signal as a performance measure in our study. A real-life application of the proposed methods is also presented in this study using real data from electrical engineering related to a grid-connected photovoltaic system. [ABSTRACT FROM AUTHOR]

Control chart is a popular technique that is widely used in statistical process control to identify any possible deviations from a stable state of a process. Shewhart charts are famous for identifying larger shifts, while cumulative sum and exponentially weighted moving average control charts are well known for identifying smaller shifts in process parameters. This study examines the performance of a sequential-based EWMA (namely S E W M A ) chart for observing the location of a normally distributed process. The performance of S E W M A is oberved by using several overall run length properties (like average, median and standard deviation). The comparative study reveals that the overall performance of the proposed design is better than the existing counterparts. Moreover, the superiority depends on the choice of the design parameters of the proposed chart. A real-life data set from a steel rod manufacturing industry were considered to show the real-life illustration of the proposed design. [ABSTRACT FROM AUTHOR]