dc.contributor.author |
Aini Abd Majid, N |
en |
dc.contributor.author |
Young, Brent |
en |
dc.contributor.author |
Taylor, Mark |
en |
dc.contributor.author |
Chen, JJJ |
en |
dc.contributor.editor |
Hamdan, AR |
en |
dc.contributor.editor |
Famili, FA |
en |
dc.contributor.editor |
Shamsuddin, SM |
en |
dc.contributor.editor |
Bakar, AA |
en |
dc.contributor.editor |
Abdullah, S |
en |
dc.contributor.editor |
Othman, ZA |
en |
dc.contributor.editor |
Nazri, MZA |
en |
dc.contributor.editor |
Kassim, MRM |
en |
dc.coverage.spatial |
Langkawi, Malaysia |
en |
dc.date.accessioned |
2017-12-19T20:43:46Z |
en |
dc.date.issued |
2012 |
en |
dc.identifier.citation |
Editors: Hamdan AR, Famili FA, Shamsuddin SM, Bakar AA, Abdullah S, Othman ZA, Nazri MZA, Kassim MRM. Proceedings 2012 4th Conference on Data Mining and Optimization (DMO). IEEE, Piscataway, New Jersey. 43-46. 2012 |
en |
dc.identifier.isbn |
9781467327183 |
en |
dc.identifier.issn |
2155-6938 |
en |
dc.identifier.uri |
http://hdl.handle.net/2292/36781 |
en |
dc.description.abstract |
Developing a fault detection and diagnosis system of complex processes usually involve large volumes of highly correlated data. In the complex aluminium smelting process, there are difficulties in isolating historical data into different classes of faults for developing a fault diagnostic model. This paper presents a new application of using a data mining tool, k-means clustering in order to determine precisely how data corresponds to different classes of faults in the aluminium smelting process. The results of applying the clustering technique on real data sets show that the boundary of each class of faults can be identified. This means the faulty data can be isolated accurately to enable for the development of a fault diagnostic model that can diagnose faults effectively. |
en |
dc.publisher |
IEEE |
en |
dc.relation.ispartof |
The 2012 4th International Conference on Data Mining and Optimization 2012 (DMO2012) |
en |
dc.relation.ispartofseries |
Proceedings 2012 4th Conference on Data Mining and Optimization (DMO) |
en |
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
en |
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
en |
dc.title |
K-means clustering pre-analysis for fault diagnosis in an aluminium smelting process |
en |
dc.type |
Conference Item |
en |
dc.identifier.doi |
10.1109/DMO.2012.6329796 |
en |
pubs.begin-page |
43 |
en |
dc.rights.holder |
Copyright: IEEE |
en |
pubs.end-page |
46 |
en |
pubs.finish-date |
2012-09-04 |
en |
pubs.place-of-publication |
Piscataway, New Jersey |
en |
pubs.publication-status |
Published |
en |
pubs.start-date |
2012-09-02 |
en |
dc.rights.accessrights |
http://purl.org/eprint/accessRights/RestrictedAccess |
en |
pubs.subtype |
Proceedings |
en |
pubs.elements-id |
368938 |
en |
pubs.org-id |
Engineering |
en |
pubs.org-id |
Chemical and Materials Eng |
en |
dc.identifier.eissn |
2155-6946 |
en |
pubs.record-created-at-source-date |
2017-12-20 |
en |
pubs.online-publication-date |
2012-10-15 |
en |