mdh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Diagnosing the Cause and Its Timing of Changes in Multivariate Process Mean Vector From Quality Control Charts Using Artificial Neural Network
Islamic Azad University, Iran.ORCID iD: 0000-0002-8524-3321
2011 (English)In: World Academy of Science, Engineering and Technology: An International Journal of Science, Engineering and Technology, ISSN 2010-376X, E-ISSN 2070-3740, Vol. 5, no 6, 212-217 p.Article in journal (Refereed) Published
Abstract [en]

Quality control charts are very effective in detecting out of control signals but when a control chart signals an out of control condition of the process mean, searching for a special cause in the vicinity of the signal time would not always lead to prompt identification of the source(s) of the out of control condition as the change point in the process parameter(s) is usually different from the signal time. It is very important to manufacturer to determine at what point and which parameters in the past caused the signal. Early warning of process change would expedite the search for the special causes and enhance quality at lower cost. In this paper the quality variables under investigation are assumed to follow a multivariate normal distribution with known means and variance-covariance matrix and the process means after one step change remain at the new level until the special cause is being identified and removed, also it is supposed that only one variable could be changed at the same time. This research applies artificial neural network (ANN) to identify the time the change occurred and the parameter which caused the change or shift. The performance of the approach was assessed through a computer simulation experiment. The results show that neural network performs effectively and equally well for the whole shift magnitude which has been considered.

Place, publisher, year, edition, pages
2011. Vol. 5, no 6, 212-217 p.
National Category
Other Engineering and Technologies
Identifiers
URN: urn:nbn:se:mdh:diva-26437Scopus ID: 2-s2.0-84855252199OAI: oai:DiVA.org:mdh-26437DiVA: diva2:760011
Available from: 2014-11-02 Created: 2014-10-31 Last updated: 2014-11-03Bibliographically approved

Open Access in DiVA

No full text

Scopus

Search in DiVA

By author/editor
Ahmadzadeh, Farzaneh
In the same journal
World Academy of Science, Engineering and Technology: An International Journal of Science, Engineering and Technology
Other Engineering and Technologies

Search outside of DiVA

GoogleGoogle Scholar

Total: 45 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf