Data mining model-based control charts for multivariate and autocorrelated processes

Kim, Seoung Bum; Jitpitaklert, Weerawat; Park, Sun-Kyoung; Hwang, Seung-June

doi:10.1016/j.eswa.2011.08.010

Cited by 28 publications

(17 citation statements)

References 20 publications

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“…• By carefully taking into account the correlation structure among variables, we may get more effective design structure that offers better detection abilities, as may be seen for Tables 1, 2, and 3. • The SDRL appear higher than the expected, and it is affected by choice of c used in Equations ( 12), ( 13), (15), and (16).…”

Section: Evaluations and Comparative Analysismentioning

confidence: 86%

On the multivariate progressive control chart for effective monitoring of covariance matrix

Ajadi

Hung

Riaz

et al. 2021

Quality & Reliability Eng

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With the development of modern acquisition techniques, data with several correlated quality characteristics are increasingly accessible. Thus, multivariate control charts can be employed to detect changes in the process. This study proposes two multivariate control charts for monitoring process variability (MPVC) using a progressive approach. First, when the process parameters are known, the performance of the MPVC charts is compared with some multivariate dispersion schemes. The results showed that the proposed MPVC charts outperform their counterparts irrespective of the shifts in the process dispersion. The effects of the Phase I estimated covariance matrix on the efficiency of the MPVC charts were also evaluated. The performances of the proposed methods and their counterparts are evaluated by calculating some useful run length properties. An application of the proposed chart is also considered for the monitoring of a carbon fiber tubing process. K E Y W O R D Sdispersion monitoring, estimation effects, multivariate control chart, phase I, progressive setup INTRODUCTIONMany manufacturing processes have multiple correlated quality characteristics; multivariate control chart is very effective in monitoring such processes. 1 For example, monitoring multivariate quality variables like the diameter and length of the manufacturing process of a dowel pin. Hotelling 2 introduced a 2 -control chart; this chart is very efficient at detecting large shifts in the process mean vector, which is also mentioned by Mahmood et al. 3 Two multivariate cumulative sum (MCUSUM) charts were proposed by Healy, 4 where the first chart monitors the process mean vector, and the other monitors the covariance matrix. Crosier 5 also proposed a multivariate CUSUM chart. Pignatiello Jr and Runger 6 introduced another multivariate CUSUM control charts for detecting shifts in the process mean vector. Lowry et al 7 provided a Multivariate exponentially weighted moving average (EWMA) chart for detecting small and intermediate shifts in the process parameter. This chart is a direct extension of the univariate EWMA control chart by Roberts. 8 The multivariate CUSUM and EWMA control charts are very efficient in detecting small and moderate shifts in the process parameters. Ajadi and Riaz 9 proposed two multivariate charts which combine the effects of MEWMA and MCUSUM charts. Their proposed charts are more effective in detecting only small shifts in the proposed than the MEWMA and MCUSUM charts. Next, multivariate dispersion charts are employed to detecting changes in the covariance matrices. For example, Alt 10 proposed generalized variance chart (GVC), |S|. The determinant of the sample covariance matrix was employed as the summary statistics. Chen et al 11 worked on a Max-MEWMA chart in monitoring the mean vector and covariance matrix 2724

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Section: Evaluations and Comparative Analysismentioning

confidence: 86%

On the multivariate progressive control chart for effective monitoring of covariance matrix

Ajadi

Hung

Riaz

et al. 2021

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…Several attempts have been made to combine classification algorithms with multivariate control charts to refine the monitoring statistics (Sukchotrat et al, 2011;Hwang et al, 2007;Chongfuangprinya et al, 2011;Kim et al, 2012). These classification-based control charts can detect out-of-control observations more accurately than traditional multivariate control charts, especially under nonnormal situations.…”

Section: Introductionmentioning

confidence: 99%

Adaptive nonparametric control chart for time-varying and multimodal processes

Kang

Kim

2016

Journal of Process Control

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Multivariate statistical process control techniques have been widely used to improve processes by reducing variation and preventing defects. In modern manufacturing, because of the complexity and variability of processes, traditional multivariate control charts such as Hotelling's T 2 cannot efficiently handle situations in which the patterns of process observations are nonlinear, multimodal, and time varying. In the present study, we propose a nonparametric control chart, which is capable of adaptively monitoring time-varying and multimodal processes. Experiments with simulated and real process data from a thin film transistor-liquid crystal display (TFT-LCD) demonstrate the effectiveness and accuracy of the proposed method.

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“…In recent years, several attempts have been proposed to integrate data mining with statistical process control (SPC) [1][2][3][4][5][6]. The objective was to overcome the limitations of traditional parametric control charts especially the normality assumption, which may not be applicable in the case of modern manufacturing systems.…”

Section: Introductionmentioning

confidence: 99%

A One-Class Classification-Based Control Chart Using the K-Means Data Description Algorithm

Gani¹,

Limam

2014

Journal of Quality and Reliability Engineering

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This paper aims to enlarge the family of one-class classification-based control charts, referred to as OC-charts, and extend their applications. We propose a new OC-chart using the K-means data description (KMDD) algorithm, referred to as KM-chart. The proposed KM-chart gives the minimum closed spherical boundary around the in-control process data. It measures the distance between the center of KMDD-based sphere and the new incoming sample to be monitored. Any sample having a distance greater than the radius of KMDD-based sphere is considered as an out-of-control sample. Phase I and II analysis of KM-chart was evaluated through a real industrial application. In a comparative study based on the average run length (ARL) criterion, KM-chart was compared with the kernel-distance based control chart, referred to as K-chart, and the k-nearest neighbor data description-based control chart, referred to as KNN-chart. Results revealed that, in terms of ARL, KM-chart performed better than KNN-chart in detecting small shifts in mean vector. Furthermore, the paper provides the MATLAB code for KM-chart, developed by the authors.

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Data mining model-based control charts for multivariate and autocorrelated processes

Cited by 28 publications

References 20 publications

On the multivariate progressive control chart for effective monitoring of covariance matrix

On the multivariate progressive control chart for effective monitoring of covariance matrix

Adaptive nonparametric control chart for time-varying and multimodal processes

A One-Class Classification-Based Control Chart Using the K-Means Data Description Algorithm

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