Multivariate multinomial<i>T</i><sup>2</sup>control chart using fuzzy approach

Fernandez, Maria Nela Pastuizaca; García, Andrés Carrión; Ruíz-Barzola, Omar

doi:10.1080/00207543.2014.983617

Cited by 17 publications

(16 citation statements)

References 19 publications

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“…In research conducted by Zaman et al, they expose a way to analyse the special causes that affect the quality of products generated by manufacturing industries [8]. Pastuizaca et al presented in their work an approach where the theory of fuzzy logic was incorporated with the multivariate CC T 2 , with the purpose of analysing the quality characteristics correlating their control status and influence through triangular membership functions (linguistic way) [9]. The analysed characteristics belong to a food processing industry, where three characteristics of "appearance", "colour", and "taste" were analysed.…”

Section: Related Workmentioning

confidence: 99%

Out-of-Control Multivariate Patterns Recognition Using D2 and SVM: A Study Case for GMAW

et al. 2021

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Industrial processes seek to improve their quality control, including new technologies and satisfying requirements for globalised markets. In this paper, we present an innovative method based on Multivariate Pattern Recognition (MVPR) and process monitoring in a real-world study case. By identifying a distinctive out-of-control multivariate pattern using the Support Vector Machines (SVM) and the Mahalanobis Distance D2 it is possible to infer the variables that disturbed the process; hence, possible faults can be predicted knowing the state of the process. The method is based on our previous work, and in this paper we present the method application for an automated process, namely, the robotic Gas Metal Arc Welding (GMAW). Results from the application indicate an overall accuracy up to 88.8%, which demonstrates the effectiveness of the method, which can also be used in other MVPR tasks.

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Section: Related Workmentioning

confidence: 99%

Out-of-Control Multivariate Patterns Recognition Using D2 and SVM: A Study Case for GMAW

et al. 2021

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“…Shu, Dang, Nguyen, Hsu & Phan (2017) proposed fuzzy control limits based on results of the resolution identity in fuzzy set theory. Soleymani & Amiri, (2017) Many other researchers have contributed to fuzzy process control works from different point of view including skewed data in fuzzy control charts (Atta, Shoraim, Yahaya, Zain & Ali, 2016;Yimnak & Intaramo, 2020), nonparametric fuzzy charts (Momeni & Shokri, 2019;Wang & Hryniewicz, 2015), flexible control charts (Pekin Alakoc & Apaydin, 2018), economic design of individual control chart (Wang & Chen, 1995;Chen, Chang & Chiu, 2008), fuzzy inference control system (Saricicek & Cimen, 2011), charts for auto correlated fuzzy observations (Sadeghpour Gildeh & Shafiee, 2015), performance of FEV theory control charts with αcut level fuzzy midrange method for three skewed distributions (Intaramo, 2012), nonrandom patterns of fuzzy control charts and fuzzy run rules (Hsu & Chen, 2001;Tannock, 2003;Gulbay & Kahraman, 2006;Chih & Kuo, 2007;Fazel Zarandi, Alaeddini & Turksen 2008;Demirli & Vijayakumar, 2010;Pekin Alakoc & Apaydin, 2013), detecting mean and variance shifts of a process (Chang & Aw, 1996;Moameni, Saghaei, & Ghorbani Salnghooch, 2012;Salnghooch, 2015;Kaya, Erdogan & Yildiz, 2017), fuzzy multivariate control charts (Taleb Limam & Hirota, 2006;Moheb Alizadeh, Arshadi Khamseh & Fatemi Ghomi, 2010;Pastuizaca Fernandez, Carrion Garcia, A. & Ruiz Barzola, 2015), multi objective design of control charts (Morabi, Owlia, Bashiri & Doroudyan, 2015), fuzzy CUSUM and EWMA control charts (Senturk, Erginel, Kaya, & Kahraman, 2014;Akhundjanov & Pascual, 2015).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Xbar and S Control Charts Based on Confidence Intervals

Alakoc

2021

Journal of Advanced Research in Natural and Applied Sciences

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There have been changes since the companies have realized the important role of quality improvement in their success. If they are able to produce high quality products and satisfy demands, then they can survive in competitive global markets. Quality improvement applications aim to decrease variability, which leads to less cost, production time, number of defects, scrap, rework and more customer satisfaction. Quality can be improved by reducing product variability. On the other hand, uncertainty or subjectivity is a part of many engineering and real life problems. However, these problems cannot be solved by traditional methods. This study focuses on constructing Xbar and S control charts in fuzzy environment. The approach is developed by considering the theoretical structure of the Shewhart control charts. The core of the approach depends on the combination of parametric interval estimation and fuzzy statistics. Control limits and samples are presented by fuzzy numbers which ensures to maintain fuzziness in control charts. An important property of the approach is that the fuzzy charts can be reduced to Shewhart control charts. A simulation study was conducted for the performance evaluation of fuzzy Xbar and S control charts. The proposed fuzzy control chart is sensitive to process mean shifts and variance changes, and outperforms the traditional control charts under the changes of variance. In addition, an example from the literature shows that the approach is an effective way of presenting fuzziness in the quality characteristics, which enables the approach to have high applicability to the real life problems.

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“…Sentürk et al proposed a univariate fuzzy EWMA chart. Alipour and Noorossana suggested the fuzzy MEWMA chart, and Pastuizaca Fernández et al developed the fuzzy multinomial T 2 chart. Chong et al proposed the distribution‐free Shewhart‐Lepage premier control charts for a joint monitoring of the location and scale parameters of a process.…”

Section: Introductionmentioning

confidence: 99%

Adaptive multivariate double sampling and variable sampling interval Hotelling's T² charts

Khatun

Khoo

Yeong

et al. 2018

Quality & Reliability Eng

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In this article, two adaptive multivariate charts, which combine the double sampling (DS) and variable sampling interval (VSI) features, called the adaptive multivariate double sampling variable sampling interval T 2 (AMDSVSI T 2 ) and the adaptive multivariate double sampling variable sampling interval combined T 2 (AMDSVSIC T 2 ) charts, are proposed. The real purpose of using the proposed charts is to provide flexibility by enabling the sampling interval length of the DS T 2 chart to be varied so that the chart's sensitivity can be enhanced. The fundamental difference between the two proposed charts is that when a second sample is taken, the AMDSVSI T 2 chart uses the information of the combined sample mean vectors while the AMDSVSIC T 2 chart uses the information of the combined T 2 statistics, in deciding about the process status. This research is motivated by existing combined DS X and VSI X charts in the literature, which show convincing performance improvement over the standard DS X chart. Consequently, it is believed that adopting this existing approach in the multivariate case will enable superior multivariate DS charts to be proposed. Numerical results show that the proposed charts outperform the existing standard T 2 and other adaptive multivariate charts, in detecting shifts in the mean vector, for the zero-state and steady-state cases. The performances of both charts when the shift sizes in the mean vector are unknown are also measured. The application of the AMDSVSI T 2 chart is illustrated with an example. KEYWORDS average time to signal, double sampling, expected average time to signal, variable sampling interval

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Multivariate multinomialT²control chart using fuzzy approach

Cited by 17 publications

References 19 publications

Out-of-Control Multivariate Patterns Recognition Using D2 and SVM: A Study Case for GMAW

Out-of-Control Multivariate Patterns Recognition Using D2 and SVM: A Study Case for GMAW

Fuzzy Xbar and S Control Charts Based on Confidence Intervals

Adaptive multivariate double sampling and variable sampling interval Hotelling's T² charts

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Multivariate multinomialT2control chart using fuzzy approach

Cited by 17 publications

References 19 publications

Out-of-Control Multivariate Patterns Recognition Using D2 and SVM: A Study Case for GMAW

Out-of-Control Multivariate Patterns Recognition Using D2 and SVM: A Study Case for GMAW

Fuzzy Xbar and S Control Charts Based on Confidence Intervals

Adaptive multivariate double sampling and variable sampling interval Hotelling's T2 charts

Contact Info

Product

Resources

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Multivariate multinomialT²control chart using fuzzy approach

Adaptive multivariate double sampling and variable sampling interval Hotelling's T² charts