Fuzzy logic based assignable cause diagnosis using control chart patterns

Abstract

a b s t r a c tControl chart patterns, besides determining the presence of assignable causes, also provide hints on the nature of assignable cause(s) present. Relating the patterns exhibited on the control chart to assignable causes is an ambiguous and vague task especially when multiple patterns co-exist. In this work, a rule based fuzzy inference system is developed for X control chart to prioritize the control chart causes based on the accumulated evidence. When a process goes out of control, search for assignable causes can be assisted by the priorities assigned to the causes. For an in-control process, developing patterns can be tracked and preventive action can be taken to prevent the process from going out of control.

Fuzzy Xbar and S Control Charts Based on Confidence Intervals

“…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).…”

Quality 4.0 in Action: Smart Hybrid Fault Diagnosis System in Plaster Production

“…Specific causes are those that cause changes and short-term fluctuations, and, if they occur, they destroy the stability of the process, which ought to be known and eliminated as quickly as time permits. Common causes are because of the inherent characteristics of the process, and, if they exist, deviations (background noise) are in control [5,6]. However, the most crucial ability of control charts is detecting various types of patterns consisting of a series of consecutive points that are observed on these charts, which reflects fluctuations in the process [7].…”

Monitoring the Process Based on Belief Statistic for Neutrosophic Gamma Distributed Product

“…In the quick monitoring of the quality of the product in this era of fast technology, being used in the production units, a minute delay can result in the production of a huge amount of defective items or items which need reworking. To maintain the production process at the required quality level, the continuous improvement of the production process and the identification of sources of the variation are the prime objectives of any process monitoring scheme [2]. Vigilant monitoring is demanded by the production process to identify the root cause and preventing it from reoccurring of unwanted situation.…”