Multioutput Least Square SVR Based Multivariate EWMA Control Chart

Khusna, Hidayatul; Mashuri, Muhammad; Suhartono, Suhartono; Prastyo, Dedy Dwi; Ahsan, Muhammad

doi:10.1088/1742-6596/1028/1/012221

Cited by 13 publications

(8 citation statements)

References 16 publications

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“…The Multivariate Exponentially Weighted Moving Average (MEWMA) approach was studied by Chen, Cheng, and Xie (2005) for simultaneously monitoring mean and variance. Pirhooshyaran and Niaki (2015) and Khusna, Mashuri, Suhartono, Prastyo, and Ahsan (2018) used the MEWMA approach for autocorrelated data, while Gunaratne, Abdollahian, Huda, and Yearwood (2017) adopted this approach to monitor the process variability for high-dimensional data. By contrast, a multivariate control chart based on attribute characteristics was developed for multiattribute processes (Chena, Chang, & Chen, 2011;Cozzucoli, 2009;Mukhopadhyay, 2008;Niaki & Abbasi, 2007a, 2007bWibawati, Mashuri, Purhadi, Irhamah, & Ahsan, 2018;Wibawati, Purhadi, & Irhamah, 2016), and for multivariate Poisson distributions (Chiu & Kuo, 2008;Laungrungrong, 2010;Niaki & Khedmati, 2013;M.…”

Section: Introductionmentioning

confidence: 99%

Multivariate control chart based on PCA mix for variable and attribute quality characteristics

Ahsan

Mashuri

Kuswanto

et al. 2018

Production & Manufacturing Research

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Two types of control charts exist based on different quality characteristics: variable and attribute. These characteristics are commonly monitored using separate procedures. Only a few studies focused on the utilization of control charts to monitor a process with mixed characteristics. This study develops a new concept of the T 2 control chart based on a Principal Component Analysis (PCA) Mix, that is a PCA method that can jointly handle continuous and categorical data. The Kernel Density Estimation (KDE) method is used to estimate the control limit. Through simulation studies, the performance of the proposed chart is evaluated using the Average Run Length (ARL). T 2 control limits obtained from KDE produce a stable ARL 0 at~370 for α ¼ 0:00273: For the shifted process, the proposed chart demonstrates excellent performance for an appropriate number of principal components used. Applications of the simulated process and real cases show that the proposed chart is sensitive to monitoring the shifted process.

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Section: Introductionmentioning

confidence: 99%

Multivariate control chart based on PCA mix for variable and attribute quality characteristics

Ahsan

Mashuri

Kuswanto

et al. 2018

Production & Manufacturing Research

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“…Karena karakteristik kualitas yang akan diuji diduga saling berhubungan dengan karakteristik kualitas lainnya, maka metode statistik yang digunakan untuk mengetahui dalam pengendalian proses yaitu diagram kendali multivariat. Terdapat beberapa tipe diagram kendali multivariat, diantaranya adalah tipe Shewhart [2]- [4], Multivariate Exponentially Weighted Moving Average (MEWMA) [5], [6] , dan Multivariate Cumulative Sum (MCUSUM) [7], [8]. Pada penelitian ini, diagram Generalized Variance dan T 2 Hotelling digunakan untuk memonitor kualtias tepung terigu.…”

Section: Pendahuluanunclassified

Pengendalian Kualitas Statistik pada Tepung Terigu Menggunakan Peta Kendali Multivariat

Arista¹,

Ramadini²,

Ahsan³

2021

Inferensi

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Persaingan di dunia industri kini semakin ketat, sehingga perlu untuk meningkatkan kualitas pada suatu produk. Pada penelitian ini, karakteristik kualitas yang ingin diuji adalah kadar Moisture, Ash, dan Gluten pada tepung terigu. Metode statistik yang digunakan untuk mengendalikan kualitas proses yaitu diagram kendali multivariat Generalized Variance dan T 2 Hotelling serta kapabilitas proses dari masing-masing karakteristik kualitas dan kapabilitas proses multivariat. Tujuannya ingin diketahui data pengamatan sudah terkendali atau tidak secara statistik, serta ingin diketahui kapabilitas prosesnya. Dari data yang diperoleh, data dibagi menjadi dua fase dengan fase 1 jumlah subgroup yang diambil sebesar 35 dan fase 2 jumlah subgroup sebanyak 5 dan masing-masing ukuran subgroup adalah 8. Hasil penelitian ini yaitu data pengamatan bersifat dependen dan data berdistribusi normal multivariat. Diagram kendali Generalized Variance belum terkendali secara statistik, begitu pula dengan peta kendali T 2 Hotelling belum terkendali secara statistik. Pada analisis kapabilitas proses pada masing-masing karakteristik kualitas, diperoleh bahwa data pengamatan kandungan moisture, ash, dan gluten pada tepung terigu belum kapabel atau belum sesuai dengan spesifikasi yang telah ditentukan.

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“…The variables that most existing multivariate control charts are designed to monitor are either purely numerical or purely categorical. With regard to monitoring numerical variables, recent developments of multivariate control charts have been motivated by the need to deal with the characteristics inherent in the numerical variables, such as non normality (Capizzi 2015), auto-correlation (Khusna et al 2018;Pirhooshyaran and Niaki 2015), and high dimensionality (Gunaratne et al 2017). When treating categorical variables, most of the papers in relevant literature consider multivariate Poisson (Aslam et al 2017;Aparisi et al 2014;Chiu and Kuo 2007) and binomial distributions (Chiu and Kuo 2010).…”

Section: Introductionmentioning

confidence: 99%

Nonparametric multivariate control chart for numerical and categorical variables

Jin

Loosveldt

2022

Communications in Statistics - Simulation and Computation

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Multivariate statistical process control (MSPC) was developed for the monitoring of variables that are either all numerical or all categorical. In the present paper, we describe a nonparametric control scheme that can be used to monitor a mixture of numerical and categorical variables simultaneously. It integrates Principal Component Analysis Mix (PCA Mix), a multivariate statistical tool, with the conventional Hotelling T 2 chart. To estimate the control limit for the PCA Mix based T 2 statistic, two nonparametric approaches -kernel density estimation (KDE) and bootstrap -are employed, because of the unknown nature of the underlying distribution. The simulation results demonstrate that with an appropriate number of principal components, both bootstrap and KDE exhibit convincing performance in terms of generating the same, or nearly the same, number of false alarms (ARL 0 ) as expected, and being able to detect process shifts efficiently (ARL 1 ). Compared with bootstrap, KDE is shown to work better with small sample sizes (n < 800) and to be slightly more sensitive to

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Multioutput Least Square SVR Based Multivariate EWMA Control Chart

Cited by 13 publications

References 16 publications

Multivariate control chart based on PCA mix for variable and attribute quality characteristics

Multivariate control chart based on PCA mix for variable and attribute quality characteristics

Pengendalian Kualitas Statistik pada Tepung Terigu Menggunakan Peta Kendali Multivariat

Nonparametric multivariate control chart for numerical and categorical variables

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