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
Multivariate process parameter change identification by neural network
Luleå University of Technology, Luleå, Sweden.ORCID iD: 0000-0002-8524-3321
Luleå University of Technology, Luleå, Sweden.
Luleå University of Technology, Luleå, Sweden.
2013 (English)In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 69, no 9-12, p. 2261-2268Article in journal (Refereed) Published
Abstract [en]

Whenever there is an out-of-control signal in process parameter control charts, maintenance engineers try to diagnose the cause near the time of the signal which does not always lead to prompt identification of the source(s) of the out-of-control condition, and this in some cases yields to extremely high monetary loses for the manufacturer owner. This paper applies multivariate exponentially weighted moving average (MEWMA) control charts and neural networks to make the signal identification more effective. The simulation of this procedure shows that this new control chart can be very effective in detecting the actual change point for all process dimension and all shift magnitudes considered. This methodology can be used in manufacturing and process industries to predict change points and expedite the search for failure causing parameters, resulting in improved quality at reduced overall cost. This research shows development of MEWMA by usage of neural network for identifying the step change-point and the variable responsible for the change in the process mean vector.

Place, publisher, year, edition, pages
2013. Vol. 69, no 9-12, p. 2261-2268
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-26431DOI: 10.1007/s00170-013-5200-xISI: 000327095900030Scopus ID: 2-s2.0-84892371787OAI: oai:DiVA.org:mdh-26431DiVA, id: diva2:759877
Available from: 2014-10-31 Created: 2014-10-31 Last updated: 2017-12-05Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Ahmadzadeh, Farzaneh

Search in DiVA

By author/editor
Ahmadzadeh, Farzaneh
In the same journal
The International Journal of Advanced Manufacturing Technology
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 47 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