A Nonparametric HEWMA-p Control Chart for Variance in Monitoring Processes

Control charts are considered as powerful tools in detecting any shift in a process. Usually, the Shewhart control chart is used when data follows the symmetrical property of a normal distribution. In practice, the data from the industry may follow a non-symmetrical distribution or an unknown distribution. The average run length (ARL) is a significant measure to assess the performance of the control chart. The ARL may mislead when the statistic is computed from an asymmetric distribution. To handle this issue, in this paper, an ARL-unbiased hybrid exponentially weighted moving average proportion (HEWMA-p) chart is proposed for monitoring the process variance for a non-normal distribution or an unknown distribution. The efficiency of the proposed chart is compared with the existing chart in terms of ARLs. The proposed chart is more efficient than the existing chart in terms of ARLs. A real example is given for the illustration of the proposed chart in the industry.11Ysciescopu

Nonparametric (distribution-free) control charts : an updated overview and some results

Control charts that are based on assumption(s) of a specific form for the underlying process distribution are referred to as parametric control charts. There are many applications where there is insufficient information to justify such assumption(s) and, consequently, control charting techniques with a minimal set of distributional assumption requirements are in high demand. To this end, nonparametric or distribution-free control charts have been proposed in recent years. The charts have stable in-control properties, are robust against outliers and can be surprisingly efficient in comparison with their parametric counterparts. Chakraborti and some of his colleagues provided review papers on nonparametric control charts in 2001, 2007 and 2011, respectively. These papers have been received with considerable interest and attention by the community. However, the literature on nonparametric statistical process/quality control/monitoring has grown exponentially and because of this rapid growth, an update is deemed necessary. In this article, we bring these reviews forward to 2017, discussing some of the latest developments in the area. Moreover, unlike the past reviews, which did not include the multivariate charts, here we review both univariate and multivariate nonparametric control charts. We end with some concluding remarks.https://www.tandfonline.com/loi/lqen20hj2020Science, Mathematics and Technology Educatio

A Multivariate Control Chart for Monitoring Several Exponential Quality Characteristics Using EWMA

In this paper, we propose a new multivariate control chart for monitoring several exponential distributed characteristics. We transform each exponential variable into the one following an approximately normal distribution. The control statistic of the proposed chart is constructed using the exponentially weighted moving average. The performance of the proposed control chart is studied using the average run length according to the process shift. Some tables are presented for bivariate and trivariate cases. The application of the proposed control chart is discussed with the help of simulated data.11Ysciescopu

A Neural Network Approach to Synthetic Control Chart for the Process Mean

In this project, a multivariate synthetic control chart for monitoring the process mean vector of skewed populations using weighted standard deviations has been proposed. The proposed chart incorporates the weighted standard deviation (WSD) method of Chang and Bai (2004) into the standard multivariate synthetic chart of Ghute and Shirke (2008)

Distribution-free double-sampling precedence monitoring scheme to detect unknown shifts in the location parameter

In most applications, parametric monitoring schemes are used to monitor the majority of industrial and nonindustrial processes in order to improve the quality of the outputs or services. However, parametric monitoring schemes are known to underperform when the normality assumption is not met or when there is not enough information about the symmetry or asymmetry nature of the process underlying distribution. Hence, in this paper, a new nonparametric Phase II Shewhart-type double-sampling (DS) monitoring scheme based on the precedence statistic is proposed in order to efficiently monitor quality processes when the underlying process distribution departs from normality. The performance is investigated using the average run length (ARL), standard deviation of the run length (SDRL), expected ARL (EARL) and expected average number of observations to signal (EANOS), and the average sample sizes (ASS) metrics. The latter metrics are computed using Monte Carlo simulation and exact formulae. In general, it is shown that the new DS precedence scheme outperforms the existing basic Shewhart precedence scheme with and without supplementary runs rules in many situations. A real-life illustrative example based on a filling process of milk bottles is provided to demonstrate the application and implementation of the new DS precedence monitoring scheme.https://wileyonlinelibrary.com/journal/qre2022-06-18hj2022Statistic

Analysis and application of selected control charts suitable for smart manufacturing processes

Nonparametric control charts (NPCC) have shown great potential for monitoring processes in conditions of smart manufacturing with complex structures, various monitored characteristics and the need to process big data. Practical applications of NPCCs are very rare. The main reasons for this situation are a deficiency in software support and a lack of simple but complete instructions for their application. The introduction of such manual, which is based on the authors' own simulations of performance of wide spectrum of NPCCs in conditions of different violations of data prerequisites, leading to recommendations for the selection of the most effective NPCC in various practical situations, is the main goal of this paper. Compared to other similar studies, this approach covers a wider range of control charts, and it was applied to a wider spectrum of data assumption violations. As an integral part of these analyses, an examination of various control chart performance indicators such as ARL, MRL, x(5) and x(95) was performed using simulations to select the best of them. The designed methodology was verified using real data.Web of Science1211art. no. 541

On the Effectiveness of Profile Monitoring to Enhance Functional Performance of Particleboards

This paper explores connection between profile monitoring and functional performance of manufactured products. In particular, the empirical relationship between the vertical density profile of the particleboards and their functional performances (the internal bond and the surface soundness) is studied. Results based on a real case study showed that the profile shape clearly affects the final performance of the panel, and thus profile monitoring is really worth to keep the final quality of the product at its target level. This result motivates the second objective of the paper, which consists of comparing performance of two (parametric and nonparametric) approaches for vertical density profile monitoring

Shewhart control schemes with supplementary 2‐of‐(h + 1) side‐sensitive runs‐rules under the Burr‐type XII distribution

Please read abstract in the article.http://wileyonlinelibrary.com/journal/qre2019-09-27hj2018Science, Mathematics and Technology Educatio