A study on average run length of fuzzy EWMA control chart
Abstract Quality control charts are one of the main features of statistical process control. The performance of control charts is assessed on the basis of the average run length (Shepherd and Shi (1998) IFAC Proc Vol 31(16):435–440. https://doi.org/10.1016/S1474-6670(17)40518-0). ARL is the average...
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| Autor*in: |
Khan, Muhammad Zahir [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 26(2022), 18 vom: 09. Juli, Seite 9117-9124 |
|---|---|
| Übergeordnetes Werk: |
volume:26 ; year:2022 ; number:18 ; day:09 ; month:07 ; pages:9117-9124 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00500-022-07310-6 |
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| 520 | |a Abstract Quality control charts are one of the main features of statistical process control. The performance of control charts is assessed on the basis of the average run length (Shepherd and Shi (1998) IFAC Proc Vol 31(16):435–440. https://doi.org/10.1016/S1474-6670(17)40518-0). ARL is the average number of sample points that must be plotted before a point shows an out-of-control condition. In-control average run length ($ ARL_{0} $) and out-of-control average run length ($ ARL_{1} $) are two types of ARLs. These values of $ ARL_{0} $ show the false alarm when the process is in control, and $ ARL_{1} $ indicates the true alarm when the process is out of control. The control chart that generates fewer ARL is considered more efficient. The exponentiated weighted moving average (EWMA) is used to detect small shifts in the process In this article comparative performance of one of the exponentially weighted moving average (EWMA) control charts are evaluated using ARLs in conventional and fuzzy environments. The novelty of the study is that the comparison between Fuzzy EWMA and conventional EWMA was made. The fuzzy EWMA chart detects a shift at the 20th sample, while the conventional EWMA chart detects the same shift at the 25th sample. A conventional and fuzzy EWMA control chart based on the real-life example of a measurement of food color is presented. | ||
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10.1007/s00500-022-07310-6 doi (DE-627)SPR047869127 (SPR)s00500-022-07310-6-e DE-627 ger DE-627 rakwb eng Khan, Muhammad Zahir verfasserin aut A study on average run length of fuzzy EWMA control chart 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Quality control charts are one of the main features of statistical process control. The performance of control charts is assessed on the basis of the average run length (Shepherd and Shi (1998) IFAC Proc Vol 31(16):435–440. https://doi.org/10.1016/S1474-6670(17)40518-0). ARL is the average number of sample points that must be plotted before a point shows an out-of-control condition. In-control average run length ($ ARL_{0} $) and out-of-control average run length ($ ARL_{1} $) are two types of ARLs. These values of $ ARL_{0} $ show the false alarm when the process is in control, and $ ARL_{1} $ indicates the true alarm when the process is out of control. The control chart that generates fewer ARL is considered more efficient. The exponentiated weighted moving average (EWMA) is used to detect small shifts in the process In this article comparative performance of one of the exponentially weighted moving average (EWMA) control charts are evaluated using ARLs in conventional and fuzzy environments. The novelty of the study is that the comparison between Fuzzy EWMA and conventional EWMA was made. The fuzzy EWMA chart detects a shift at the 20th sample, while the conventional EWMA chart detects the same shift at the 25th sample. A conventional and fuzzy EWMA control chart based on the real-life example of a measurement of food color is presented. Control chart (dpeaa)DE-He213 EWMA statistics (dpeaa)DE-He213 Simulation (dpeaa)DE-He213 Average run length (dpeaa)DE-He213 Shift (dpeaa)DE-He213 Khan, Muhammad Farid aut Aslam, Muhammad (orcid)0000-0003-0644-1950 aut Mughal, Abdur Razzaque aut Enthalten in Soft Computing Springer-Verlag, 2003 26(2022), 18 vom: 09. Juli, Seite 9117-9124 (DE-627)SPR006469531 nnns volume:26 year:2022 number:18 day:09 month:07 pages:9117-9124 https://dx.doi.org/10.1007/s00500-022-07310-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 26 2022 18 09 07 9117-9124 |
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10.1007/s00500-022-07310-6 doi (DE-627)SPR047869127 (SPR)s00500-022-07310-6-e DE-627 ger DE-627 rakwb eng Khan, Muhammad Zahir verfasserin aut A study on average run length of fuzzy EWMA control chart 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Quality control charts are one of the main features of statistical process control. The performance of control charts is assessed on the basis of the average run length (Shepherd and Shi (1998) IFAC Proc Vol 31(16):435–440. https://doi.org/10.1016/S1474-6670(17)40518-0). ARL is the average number of sample points that must be plotted before a point shows an out-of-control condition. In-control average run length ($ ARL_{0} $) and out-of-control average run length ($ ARL_{1} $) are two types of ARLs. These values of $ ARL_{0} $ show the false alarm when the process is in control, and $ ARL_{1} $ indicates the true alarm when the process is out of control. The control chart that generates fewer ARL is considered more efficient. The exponentiated weighted moving average (EWMA) is used to detect small shifts in the process In this article comparative performance of one of the exponentially weighted moving average (EWMA) control charts are evaluated using ARLs in conventional and fuzzy environments. The novelty of the study is that the comparison between Fuzzy EWMA and conventional EWMA was made. The fuzzy EWMA chart detects a shift at the 20th sample, while the conventional EWMA chart detects the same shift at the 25th sample. A conventional and fuzzy EWMA control chart based on the real-life example of a measurement of food color is presented. Control chart (dpeaa)DE-He213 EWMA statistics (dpeaa)DE-He213 Simulation (dpeaa)DE-He213 Average run length (dpeaa)DE-He213 Shift (dpeaa)DE-He213 Khan, Muhammad Farid aut Aslam, Muhammad (orcid)0000-0003-0644-1950 aut Mughal, Abdur Razzaque aut Enthalten in Soft Computing Springer-Verlag, 2003 26(2022), 18 vom: 09. Juli, Seite 9117-9124 (DE-627)SPR006469531 nnns volume:26 year:2022 number:18 day:09 month:07 pages:9117-9124 https://dx.doi.org/10.1007/s00500-022-07310-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 26 2022 18 09 07 9117-9124 |
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10.1007/s00500-022-07310-6 doi (DE-627)SPR047869127 (SPR)s00500-022-07310-6-e DE-627 ger DE-627 rakwb eng Khan, Muhammad Zahir verfasserin aut A study on average run length of fuzzy EWMA control chart 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Quality control charts are one of the main features of statistical process control. The performance of control charts is assessed on the basis of the average run length (Shepherd and Shi (1998) IFAC Proc Vol 31(16):435–440. https://doi.org/10.1016/S1474-6670(17)40518-0). ARL is the average number of sample points that must be plotted before a point shows an out-of-control condition. In-control average run length ($ ARL_{0} $) and out-of-control average run length ($ ARL_{1} $) are two types of ARLs. These values of $ ARL_{0} $ show the false alarm when the process is in control, and $ ARL_{1} $ indicates the true alarm when the process is out of control. The control chart that generates fewer ARL is considered more efficient. The exponentiated weighted moving average (EWMA) is used to detect small shifts in the process In this article comparative performance of one of the exponentially weighted moving average (EWMA) control charts are evaluated using ARLs in conventional and fuzzy environments. The novelty of the study is that the comparison between Fuzzy EWMA and conventional EWMA was made. The fuzzy EWMA chart detects a shift at the 20th sample, while the conventional EWMA chart detects the same shift at the 25th sample. A conventional and fuzzy EWMA control chart based on the real-life example of a measurement of food color is presented. Control chart (dpeaa)DE-He213 EWMA statistics (dpeaa)DE-He213 Simulation (dpeaa)DE-He213 Average run length (dpeaa)DE-He213 Shift (dpeaa)DE-He213 Khan, Muhammad Farid aut Aslam, Muhammad (orcid)0000-0003-0644-1950 aut Mughal, Abdur Razzaque aut Enthalten in Soft Computing Springer-Verlag, 2003 26(2022), 18 vom: 09. Juli, Seite 9117-9124 (DE-627)SPR006469531 nnns volume:26 year:2022 number:18 day:09 month:07 pages:9117-9124 https://dx.doi.org/10.1007/s00500-022-07310-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 26 2022 18 09 07 9117-9124 |
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study on average run length of fuzzy ewma control chart |
| title_auth |
A study on average run length of fuzzy EWMA control chart |
| abstract |
Abstract Quality control charts are one of the main features of statistical process control. The performance of control charts is assessed on the basis of the average run length (Shepherd and Shi (1998) IFAC Proc Vol 31(16):435–440. https://doi.org/10.1016/S1474-6670(17)40518-0). ARL is the average number of sample points that must be plotted before a point shows an out-of-control condition. In-control average run length ($ ARL_{0} $) and out-of-control average run length ($ ARL_{1} $) are two types of ARLs. These values of $ ARL_{0} $ show the false alarm when the process is in control, and $ ARL_{1} $ indicates the true alarm when the process is out of control. The control chart that generates fewer ARL is considered more efficient. The exponentiated weighted moving average (EWMA) is used to detect small shifts in the process In this article comparative performance of one of the exponentially weighted moving average (EWMA) control charts are evaluated using ARLs in conventional and fuzzy environments. The novelty of the study is that the comparison between Fuzzy EWMA and conventional EWMA was made. The fuzzy EWMA chart detects a shift at the 20th sample, while the conventional EWMA chart detects the same shift at the 25th sample. A conventional and fuzzy EWMA control chart based on the real-life example of a measurement of food color is presented. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
| abstractGer |
Abstract Quality control charts are one of the main features of statistical process control. The performance of control charts is assessed on the basis of the average run length (Shepherd and Shi (1998) IFAC Proc Vol 31(16):435–440. https://doi.org/10.1016/S1474-6670(17)40518-0). ARL is the average number of sample points that must be plotted before a point shows an out-of-control condition. In-control average run length ($ ARL_{0} $) and out-of-control average run length ($ ARL_{1} $) are two types of ARLs. These values of $ ARL_{0} $ show the false alarm when the process is in control, and $ ARL_{1} $ indicates the true alarm when the process is out of control. The control chart that generates fewer ARL is considered more efficient. The exponentiated weighted moving average (EWMA) is used to detect small shifts in the process In this article comparative performance of one of the exponentially weighted moving average (EWMA) control charts are evaluated using ARLs in conventional and fuzzy environments. The novelty of the study is that the comparison between Fuzzy EWMA and conventional EWMA was made. The fuzzy EWMA chart detects a shift at the 20th sample, while the conventional EWMA chart detects the same shift at the 25th sample. A conventional and fuzzy EWMA control chart based on the real-life example of a measurement of food color is presented. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract Quality control charts are one of the main features of statistical process control. The performance of control charts is assessed on the basis of the average run length (Shepherd and Shi (1998) IFAC Proc Vol 31(16):435–440. https://doi.org/10.1016/S1474-6670(17)40518-0). ARL is the average number of sample points that must be plotted before a point shows an out-of-control condition. In-control average run length ($ ARL_{0} $) and out-of-control average run length ($ ARL_{1} $) are two types of ARLs. These values of $ ARL_{0} $ show the false alarm when the process is in control, and $ ARL_{1} $ indicates the true alarm when the process is out of control. The control chart that generates fewer ARL is considered more efficient. The exponentiated weighted moving average (EWMA) is used to detect small shifts in the process In this article comparative performance of one of the exponentially weighted moving average (EWMA) control charts are evaluated using ARLs in conventional and fuzzy environments. The novelty of the study is that the comparison between Fuzzy EWMA and conventional EWMA was made. The fuzzy EWMA chart detects a shift at the 20th sample, while the conventional EWMA chart detects the same shift at the 25th sample. A conventional and fuzzy EWMA control chart based on the real-life example of a measurement of food color is presented. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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| title_short |
A study on average run length of fuzzy EWMA control chart |
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https://dx.doi.org/10.1007/s00500-022-07310-6 |
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| author2 |
Khan, Muhammad Farid Aslam, Muhammad Mughal, Abdur Razzaque |
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Khan, Muhammad Farid Aslam, Muhammad Mughal, Abdur Razzaque |
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| doi_str |
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2024-07-03T15:31:37.126Z |
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7.4001484 |