A Fuzzy EWMA Attribute Control Chart to Monitor Process Mean

Conventional control charts are one of the most important techniques in statistical process control which are used to assess the performance of processes to see whether they are in- or out-of-control. As traditional control charts deal with crisp data, they are not suitable to study unclear, vague,...
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Gespeichert in:

Autor*in:	Muhammad Zahir Khan [verfasserIn] Muhammad Farid Khan [verfasserIn] Muhammad Aslam [verfasserIn] Seyed Taghi Akhavan Niaki [verfasserIn] Abdur Razzaque Mughal [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	crisp data fuzzy data fuzzy control charts EWMA charts fuzzy EWMA charts

Übergeordnetes Werk:	In: Information - MDPI AG, 2010, 9(2018), 12, p 312
Übergeordnetes Werk:	volume:9 ; year:2018 ; number:12, p 312

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/info9120312

Katalog-ID:	DOAJ046840575

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allfieldsGer	10.3390/info9120312 doi (DE-627)DOAJ046840575 (DE-599)DOAJf557dd5b71e64b9880ac30c3e04814ac DE-627 ger DE-627 rakwb eng T58.5-58.64 Muhammad Zahir Khan verfasserin aut A Fuzzy EWMA Attribute Control Chart to Monitor Process Mean 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Conventional control charts are one of the most important techniques in statistical process control which are used to assess the performance of processes to see whether they are in- or out-of-control. As traditional control charts deal with crisp data, they are not suitable to study unclear, vague, and fuzzy data. In many real-world applications, however, the data to be used in a control charting method are not crisp since they are approximated due to environmental uncertainties and systematic ambiguities involved in the systems under investigation. In these situations, fuzzy numbers and linguistic variables are used to grab such uncertainties. That is why the use of a fuzzy control chart, in which fuzzy data are used, is justified. As an exponentially weighted moving average (EWMA) scheme is usually used to detect small shifts, in this paper a fuzzy EWMA (F-EWMA) control chart is proposed to detect small shifts in the process mean when fuzzy data are available. The application of the newly developed fuzzy control chart is illustrated using real-life data. crisp data fuzzy data fuzzy control charts EWMA charts fuzzy EWMA charts Information technology Muhammad Farid Khan verfasserin aut Muhammad Aslam verfasserin aut Seyed Taghi Akhavan Niaki verfasserin aut Abdur Razzaque Mughal verfasserin aut In Information MDPI AG, 2010 9(2018), 12, p 312 (DE-627)654746753 (DE-600)2599790-7 20782489 nnns volume:9 year:2018 number:12, p 312 https://doi.org/10.3390/info9120312 kostenfrei https://doaj.org/article/f557dd5b71e64b9880ac30c3e04814ac kostenfrei https://www.mdpi.com/2078-2489/9/12/312 kostenfrei https://doaj.org/toc/2078-2489 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2018 12, p 312
allfieldsSound	10.3390/info9120312 doi (DE-627)DOAJ046840575 (DE-599)DOAJf557dd5b71e64b9880ac30c3e04814ac DE-627 ger DE-627 rakwb eng T58.5-58.64 Muhammad Zahir Khan verfasserin aut A Fuzzy EWMA Attribute Control Chart to Monitor Process Mean 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Conventional control charts are one of the most important techniques in statistical process control which are used to assess the performance of processes to see whether they are in- or out-of-control. As traditional control charts deal with crisp data, they are not suitable to study unclear, vague, and fuzzy data. In many real-world applications, however, the data to be used in a control charting method are not crisp since they are approximated due to environmental uncertainties and systematic ambiguities involved in the systems under investigation. In these situations, fuzzy numbers and linguistic variables are used to grab such uncertainties. That is why the use of a fuzzy control chart, in which fuzzy data are used, is justified. As an exponentially weighted moving average (EWMA) scheme is usually used to detect small shifts, in this paper a fuzzy EWMA (F-EWMA) control chart is proposed to detect small shifts in the process mean when fuzzy data are available. The application of the newly developed fuzzy control chart is illustrated using real-life data. crisp data fuzzy data fuzzy control charts EWMA charts fuzzy EWMA charts Information technology Muhammad Farid Khan verfasserin aut Muhammad Aslam verfasserin aut Seyed Taghi Akhavan Niaki verfasserin aut Abdur Razzaque Mughal verfasserin aut In Information MDPI AG, 2010 9(2018), 12, p 312 (DE-627)654746753 (DE-600)2599790-7 20782489 nnns volume:9 year:2018 number:12, p 312 https://doi.org/10.3390/info9120312 kostenfrei https://doaj.org/article/f557dd5b71e64b9880ac30c3e04814ac kostenfrei https://www.mdpi.com/2078-2489/9/12/312 kostenfrei https://doaj.org/toc/2078-2489 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2018 12, p 312
language	English
source	In Information 9(2018), 12, p 312 volume:9 year:2018 number:12, p 312
sourceStr	In Information 9(2018), 12, p 312 volume:9 year:2018 number:12, p 312
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	crisp data fuzzy data fuzzy control charts EWMA charts fuzzy EWMA charts Information technology
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container_title	Information
authorswithroles_txt_mv	Muhammad Zahir Khan @@aut@@ Muhammad Farid Khan @@aut@@ Muhammad Aslam @@aut@@ Seyed Taghi Akhavan Niaki @@aut@@ Abdur Razzaque Mughal @@aut@@
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author	Muhammad Zahir Khan
spellingShingle	Muhammad Zahir Khan misc T58.5-58.64 misc crisp data misc fuzzy data misc fuzzy control charts misc EWMA charts misc fuzzy EWMA charts misc Information technology A Fuzzy EWMA Attribute Control Chart to Monitor Process Mean
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topic	misc T58.5-58.64 misc crisp data misc fuzzy data misc fuzzy control charts misc EWMA charts misc fuzzy EWMA charts misc Information technology
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title	A Fuzzy EWMA Attribute Control Chart to Monitor Process Mean
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title_auth	A Fuzzy EWMA Attribute Control Chart to Monitor Process Mean
abstract	Conventional control charts are one of the most important techniques in statistical process control which are used to assess the performance of processes to see whether they are in- or out-of-control. As traditional control charts deal with crisp data, they are not suitable to study unclear, vague, and fuzzy data. In many real-world applications, however, the data to be used in a control charting method are not crisp since they are approximated due to environmental uncertainties and systematic ambiguities involved in the systems under investigation. In these situations, fuzzy numbers and linguistic variables are used to grab such uncertainties. That is why the use of a fuzzy control chart, in which fuzzy data are used, is justified. As an exponentially weighted moving average (EWMA) scheme is usually used to detect small shifts, in this paper a fuzzy EWMA (F-EWMA) control chart is proposed to detect small shifts in the process mean when fuzzy data are available. The application of the newly developed fuzzy control chart is illustrated using real-life data.
abstractGer	Conventional control charts are one of the most important techniques in statistical process control which are used to assess the performance of processes to see whether they are in- or out-of-control. As traditional control charts deal with crisp data, they are not suitable to study unclear, vague, and fuzzy data. In many real-world applications, however, the data to be used in a control charting method are not crisp since they are approximated due to environmental uncertainties and systematic ambiguities involved in the systems under investigation. In these situations, fuzzy numbers and linguistic variables are used to grab such uncertainties. That is why the use of a fuzzy control chart, in which fuzzy data are used, is justified. As an exponentially weighted moving average (EWMA) scheme is usually used to detect small shifts, in this paper a fuzzy EWMA (F-EWMA) control chart is proposed to detect small shifts in the process mean when fuzzy data are available. The application of the newly developed fuzzy control chart is illustrated using real-life data.
abstract_unstemmed	Conventional control charts are one of the most important techniques in statistical process control which are used to assess the performance of processes to see whether they are in- or out-of-control. As traditional control charts deal with crisp data, they are not suitable to study unclear, vague, and fuzzy data. In many real-world applications, however, the data to be used in a control charting method are not crisp since they are approximated due to environmental uncertainties and systematic ambiguities involved in the systems under investigation. In these situations, fuzzy numbers and linguistic variables are used to grab such uncertainties. That is why the use of a fuzzy control chart, in which fuzzy data are used, is justified. As an exponentially weighted moving average (EWMA) scheme is usually used to detect small shifts, in this paper a fuzzy EWMA (F-EWMA) control chart is proposed to detect small shifts in the process mean when fuzzy data are available. The application of the newly developed fuzzy control chart is illustrated using real-life data.
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title_short	A Fuzzy EWMA Attribute Control Chart to Monitor Process Mean
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