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International Journal of Production Research Volume 46, 2008 - Issue 21
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Original Articles

Using an MQE chart based on a self-organizing map NN to monitor out-of-control signals in manufacturing processes

J. B. Yu School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai, 200240, PR ChinaCorrespondence[email protected]
&
L. F. Xi School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai, 200240, PR China
Pages 5907-5933 | Received 01 Mar 2007, Published online: 10 Oct 2008

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Read on this site (2)

Guangzhou Diao, Liping Zhao & Yiyong Yao. (2015) A dynamic quality control approach by improving dominant factors based on improved principal component analysis. International Journal of Production Research 53:14, pages 4287-4303.
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Jianbo Yu & Jianping Liu. (2011) LRProb control chart based on logistic regression for monitoring mean shifts of auto-correlated manufacturing processes. International Journal of Production Research 49:8, pages 2301-2326.
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Articles from other publishers (17)

Jianbo Yu & Yue Zhang. (2022) Challenges and opportunities of deep learning-based process fault detection and diagnosis: a review. Neural Computing and Applications 35:1, pages 211-252.
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Su Kuang, Junhao Wu, Junming Li & Jianhua Tao. 2021. Proceedings of IncoME-V & CEPE Net-2020. Proceedings of IncoME-V & CEPE Net-2020 798 808 .
Beixin Xia, Zheng Jian & Ningrong Tao. (2019) An effective combined multivariate control chart based on support vector data description. Journal of Ambient Intelligence and Humanized Computing 10:12, pages 4819-4835.
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Shumei Chen & Jianbo Yu. (2019) Deep recurrent neural network‐based residual control chart for autocorrelated processes. Quality and Reliability Engineering International 35:8, pages 2687-2708.
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Beixin Xia, Zheng Jian, Lei Liu & Long Li. (2018) An effective multivariate control chart for detecting small mean shifts using support vector data description. Advances in Mechanical Engineering 10:11, pages 168781401881062.
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Ye Tian, Zili Wang, Lipin Zhang, Chen Lu & Jian Ma. (2018) A subspace learning-based feature fusion and open-set fault diagnosis approach for machinery components. Advanced Engineering Informatics 36, pages 194-206.
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Xiaodong Jia, Chao Jin, Matt Buzza, Yuan Di, David Siegel & Jay Lee. (2018) A deviation based assessment methodology for multiple machine health patterns classification and fault detection. Mechanical Systems and Signal Processing 99, pages 244-261.
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Izabela Rojek, Agnieszka Kujawińska, Adam Hamrol & Michał Rogalewicz. 2018. Intelligent Systems in Production Engineering and Maintenance – ISPEM 2017. Intelligent Systems in Production Engineering and Maintenance – ISPEM 2017 168 178 .
Young Jae Choung, Jihoon Kang & Seoung Bum Kim. (2017) Process control of time-varying systems using parameter-less self-organizing maps. Journal of Process Control 52, pages 45-56.
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Wei-Lun Chang, Arleen N. Diaz & Patrick C. K. Hung. (2014) Estimating trust value: A social network perspective. Information Systems Frontiers 17:6, pages 1381-1400.
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Wenjing Jin, Zhe Shi, David Siegel, Pierre Dersin, Cyril Douziech, Michele Pugnaloni, Piero La Cascia & Jay Lee. (2015) Development and evaluation of health monitoring techniques for railway point machines. Development and evaluation of health monitoring techniques for railway point machines.
Yi Bing Li & Fei Pan. (2013) Study on the Combination of SOM and K-Means Algorithms in Manufacturing Process Quality Control. Applied Mechanics and Materials 427-429, pages 1315-1318.
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Walid W. Nasr, Bacel Maddah & Moueen K. Salameh. (2013) EOQ with a correlated binomial supply. International Journal of Production Economics 144:1, pages 248-255.
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Shijin Wang, Jianbo Yu, Edzel Lapira & Jay Lee. (2013) A modified support vector data description based novelty detection approach for machinery components. Applied Soft Computing 13:2, pages 1193-1205.
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Ussanee Purintrapiban & H.W. Corley. (2012) Neural networks for detecting cyclic behavior in autocorrelated process. Computers & Industrial Engineering 62:4, pages 1093-1108.
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Jianbo Yu. (2011) Fault Detection Using Principal Components-Based Gaussian Mixture Model for Semiconductor Manufacturing Processes. IEEE Transactions on Semiconductor Manufacturing 24:3, pages 432-444.
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Jianbo Yu. (2010) Hidden Markov models combining local and global information for nonlinear and multimodal process monitoring. Journal of Process Control 20:3, pages 344-359.
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