A New Robust Multivariate EWMA Dispersion Control Chart for Individual Observations

Ajadi, Jimoh Olawale; Zwetsloot, Inez Maria; Tsui, Kwok‐Leung

doi:10.3390/math9091038

Cited by 8 publications

(6 citation statements)

References 22 publications

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“…The research suggests that both the Quantile-Zone distribution and Zone distribution hold significant potential for applications in quality control. This recognition aligns with the growing importance of quality control as a notable research topic, both in theory and practical application [11][12][13][14][15][16][17].…”

Section: Introductionsupporting

confidence: 66%

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Discrete Parameter-Free Zone Distribution and Its Application in Normality Testing

Avdović,

Jevremović

2023

Axioms

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In recent research endeavors, discrete models have gained considerable attention, even in cases where the observed variables are continuous. These variables can often be effectively approximated by a normal distribution. Given the prevalence of processes requiring robust quality control, models associated with the normal distribution have found widespread applicability; nevertheless, there remains a persistent need for enhanced accuracy in normality analysis, prompting the exploration of novel and improved solutions. This paper introduces a discrete parameter-free distribution linked to the normal distribution, derived from a quality control methodology rooted in the renowned ‘3-sigma’ rule. The development of a novel normality test, based on this distribution, is presented. A comprehensive examination encompasses mathematical derivation, distribution tables generated through Monte Carlo simulation studies, properties, power analysis, and comparative analysis, all with key features illustrated graphically. Notably, the proposed normality test surpasses conventional methods in performance. Termed the ‘Zone distribution’, this newly introduced distribution, along with its accompanying ‘Zone test’, demonstrates superior efficacy through illustrative examples. This research contributes a valuable tool to the field of normality analysis, offering a robust alternative for applications requiring precise and reliable assessments.

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Section: Introductionsupporting

confidence: 66%

“…The outcomes of this research have broader implications for improving quality control achieved by control charts. The relevance of these innovations is underscored by the findings reported in recent publications [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]19,21,26,29].…”

Section: Introductionmentioning

confidence: 99%

Discrete Parameter-Free Zone Distribution and Its Application in Normality Testing

Avdović,

Jevremović

2023

Axioms

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“…Researchers have examined the effect of estimating parameters on control chart applications in recent years. [1][2][3][4] In 1947, Hotelling pioneered the field of multivariate control charts for location monitoring with the introduction of the Hotelling 𝑇 2 scheme. The Hotelling 𝑇 2 chart, an extension of the Shewhart chart in the univariate setup, is a memory-less chart, which uses only the present information for constructing its statistics.…”

Section: Introductionmentioning

confidence: 99%

“…These two factors play an essential role in the effectiveness of control charts, whether univariate or multivariate, for location or dispersion monitoring charts. Researchers have examined the effect of estimating parameters on control chart applications in recent years 1–4 …”

Section: Introductionmentioning

confidence: 99%

Detecting outliers in the multivariate control charts for dispersion monitoring

Ajadi,

Raji,

Abbas

et al. 2024

Quality & Reliability Eng

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Outlier detection is an important aspect of statistical process monitoring (SPM) because outliers affect the performance of control charts. SPM researchers study the negative impact of outliers on control charts for monitoring location parameters. However, there is little research on outlier detection in multivariate charts for monitoring process dispersion. This study aims to investigate the impact of outliers in multivariate control charts for monitoring covariance matrix of a process, and then to recommend techniques for detecting potential outliers present from Phase I samples. We propose a new multivariate dispersion chart that employs the determinant of logarithm of estimated covariance matrix as the monitoring statistic. Through Monte Carlo simulations, the results show how outliers from the first phase affect the overall performance of multivariate charts. The results also demonstrate that the minimum volume ellipsoid (MVE) estimator is effective in reducing the effect of outliers on the proposed control scheme than the other compared estimators.

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“…The earliest control chart is the Shewhart control chart, but it is not sensitive to small quality parameters [24]. In order to solve its drawbacks, researchers have developed the CUSUM (cumulative sum control) and EWMA (exponential weighted moving averages) chart [25][26][27][28][29]. However, these control charts are still only post-analysis control methods; most of the established control limits consider, to a lesser extent, the stage characteristic of the process, which cause shortcomings for timeliness and cannot accurately judge or respond to abnormal conditions immediately [30].…”

Section: Introductionmentioning

confidence: 99%

State Monitoring Method for Tool Wear in Aerospace Manufacturing Processes Based on a Convolutional Neural Network (CNN)

2021

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In the aerospace manufacturing field, tool conditions are essential to ensure the production quality for aerospace parts and reduce processing failures. Therefore, it is extremely necessary to develop a suitable tool condition monitoring method. Thus, we propose a tool wear process state monitoring method for aerospace manufacturing processes based on convolutional neural networks to recognize intermediate abnormal states in multi-stage processes. There are two innovations and advantages of the proposed approach: one is that the criteria for judging abnormal conditions are extended, which is more useful for practical application. The other is that the proposed approach solved the influence of feature-to-recognition stability. Firstly, the tool wear level was divided into different state modes according to the probability density interval based on the kernel density estimation (KDE), and the corresponding state modes were connected to obtain the point-to-point control limit. Then, the state recognition model based on a convolutional neural network (CNN) was developed, and the sensitivity of the monitoring window was considered in the model. Finally, open-source datasets were used to verify the feasibility of the proposed method, and the results demonstrated the applicability of the proposed method in practice for tool condition monitoring.

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A New Robust Multivariate EWMA Dispersion Control Chart for Individual Observations

Cited by 8 publications

References 22 publications

Discrete Parameter-Free Zone Distribution and Its Application in Normality Testing

Discrete Parameter-Free Zone Distribution and Its Application in Normality Testing

Detecting outliers in the multivariate control charts for dispersion monitoring

State Monitoring Method for Tool Wear in Aerospace Manufacturing Processes Based on a Convolutional Neural Network (CNN)

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