Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6090
Title: A hybrid fuzzy adaptive sampling - Run rules for Shewhart control charts
Authors: Zarandi, Mohammad Hossein Fazel
Alaeddini, A.
Türkşen, İsmail Burhan
Keywords: statistical process control
control charts
run rules
adaptive sampling
fuzzy modeling
fuzzy inference
genetic algorithms
Issue Date: 2008
Publisher: Elsevier Science Inc
Abstract: In crisp run control rules, usually it is stated that a process moves very sharply from in-control condition to out-of-control act. This causes an increase in both false-alarm rate and control chart sensitivity. Moreover, the classical run control rules are not implemented on an intelligent sampling strategy that changes control charts' parameters to reduce error probability when the process appears to have a shift in parameter values. This paper presents a new hybrid method based on a combination of fuzzified sensitivity criteria and fuzzy adaptive sampling rules, which make the control charts more sensitive and proactive while keeping false alarms rate acceptably low. The procedure is based on a simple strategy that includes varying control chart parameters (sample size and sample interval) based on current fuzzified state of the process and makes inference about the state of process based on fuzzified run rules. Furthermore, in this paper, the performance of the proposed method is examined and compared with both conventional run rules and adaptive sampling schemes. (c) 2007 Elsevier Inc. All rights reserved.
URI: https://doi.org/10.1016/j.ins.2007.09.028
https://hdl.handle.net/20.500.11851/6090
ISSN: 0020-0255
1872-6291
Appears in Collections:Endüstri Mühendisliği Bölümü / Department of Industrial Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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