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Communications in Statistics - Simulation and Computation Volume 39, 2010 - Issue 3
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Original Articles

One-Class Classification-Based Control Charts for Monitoring Autocorrelated Multivariate Processes

Seoung Bum Kim Division of Information Management Engineering, Korea University, Seoul, KoreaCorrespondence[email protected]
,
Weerawat Jitpitaklert Department of Industrial and Manufacturing Systems Engineering, University of Texas at Arlington, Arlington, Texas, USA
&
Thuntee Sukchotrat Department of Industrial and Manufacturing Systems Engineering, University of Texas at Arlington, Arlington, Texas, USA
Pages 461-474 | Received 15 Jul 2009, Accepted 11 Nov 2009, Published online: 19 Feb 2010

Citations (16)

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Read on this site (2)

Roberto Campos Leoni, Marcela Aparecida Guerreiro Machado & Antonio Fernando Branco Costa. (2016) The T2 chart with mixed samples to control bivariate autocorrelated processes. International Journal of Production Research 54:11, pages 3294-3310.
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Maria Weese, Waldyn Martinez, Fadel M. Megahed & L. Allison Jones-Farmer. (2016) Statistical Learning Methods Applied to Process Monitoring: An Overview and Perspective. Journal of Quality Technology 48:1, pages 4-24.
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Articles from other publishers (14)

Chang Kyeom Kim, Min Hyeok Yoon & Sangyeol Lee. (2024) Robust control chart for nonlinear conditionally heteroscedastic time series based on Huber support vector regression. PLOS ONE 19:2, pages e0299120.
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Phuong Hanh Tran, Adel Ahmadi Nadi, Thi Hien Nguyen, Kim Duc Tran & Kim Phuc Tran. 2022. Control Charts and Machine Learning for Anomaly Detection in Manufacturing. Control Charts and Machine Learning for Anomaly Detection in Manufacturing 7 42 .
Dhouha Mejri, Mohamed Limam & Claus Weihs. (2021) A new time adjusting control limits chart for concept drift detection. IFAC Journal of Systems and Control 17, pages 100170.
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Weipeng Huang, Shaowu Lu, Bao Song, Yajie Ma, Fengxing Zhou & Xiaoqi Tang. (2021) Quick detection of product quality based on clustering hypersphere model. Computers & Electrical Engineering 92, pages 107179.
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Weipeng Huang, Shaowu Lu & Xiaoqi Tang. (2021) A Method Using Clustering and SVDD for Quality Detection. A Method Using Clustering and SVDD for Quality Detection.
Seokho Kang, Dongil Kim & Sungzoon Cho. (2019) Approximate training of one-class support vector machines using expected margin. Computers & Industrial Engineering 130, pages 772-778.
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Jaehong Yu & Seokho Kang. (2019) Clustering-based proxy measure for optimizing one-class classifiers. Pattern Recognition Letters 117, pages 37-44.
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Felipe Domingues Simões, Roberto Campos Leoni, Marcela Aparecida Guerreiro Machado & Antonio Fernando Branco Costa. (2016) Synthetic charts to control bivariate processes with autocorrelated data. Computers & Industrial Engineering 97, pages 15-25.
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Kaixiang Peng, Kai Zhang, Xiao He, Gang Li & Xu Yang. (2014) New kernel independent and principal components analysis‐based process monitoring approach with application to hot strip mill process. IET Control Theory & Applications 8:16, pages 1723-1731.
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Walid Gani & Mohamed Limam. (2014) A One-Class Classification-Based Control Chart Using the -Means Data Description Algorithm . Journal of Quality and Reliability Engineering 2014, pages 1-9.
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Walid Gani & Mohamed Limam. (2012) Performance Evaluation of One‐Class Classification‐based Control Charts through an Industrial Application. Quality and Reliability Engineering International 29:6, pages 841-854.
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Wen-jie Xu & Jin Yao. (2013) Integrated Quality Diagnosis Algorithm Method based on Neural Network and Sensitivity Analysis to Input Parameters. Journal of Networks 8:6.
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Chi Zhang & Zhen He. 2013. Proceedings of 2012 3rd International Asia Conference on Industrial Engineering and Management Innovation (IEMI2012). Proceedings of 2012 3rd International Asia Conference on Industrial Engineering and Management Innovation (IEMI2012) 225 232 .
Si-Jeo Park, Cheong-Sool Park, Sung-Shick Kim & Jun-Geol Baek. (2011) Model Parameter Based Fault Detection for Time-series Data. Journal of the Korea Society for Simulation 20:4, pages 67-79.
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