Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61459
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dc.contributor.authorFan, Zongwenen
dc.contributor.authorChiong, Raymonden
dc.contributor.authorHu, Zhongyien
dc.contributor.authorLin, Yuqingen
dc.date.accessioned2024-07-10T01:05:41Z-
dc.date.available2024-07-10T01:05:41Z-
dc.date.issued2017-
dc.identifier.citation1st International Conference on Computer and Drone Applications: Ethical Integration of Computer and Drone Technology for Humanity Sustainability, IConDA 2017, p. 82-86en
dc.identifier.isbn9781538607657en
dc.identifier.isbn9781538607640en
dc.identifier.isbn9781538607640en
dc.identifier.urihttps://hdl.handle.net/1959.11/61459-
dc.description.abstract<p>Anomaly detection is very important for the mining industry. If anomalies in mining equipment can be correctly detected to predict machine breakdown, mining companies will be able to reduce the cost of maintaining their machines. However, anomaly detection in this context is quite difficult considering the large volume of sensor data involved and unlabelled nature of the data. Clustering techniques have therefore been applied to analyse this problem, by dividing the data into normal and abnormal clusters. In this paper, we investigate the influence of using different numbers of clusters in clustering models, which include k-means, fuzzy c-means and the self-organising map, to obtain useful data patterns and classify the data into normal and abnormal types. Our aim here is to reduce the trigger of false alarm in the anomaly detection process. The data used in this study is based on real-world grease cycle data from a mining company in Australia. Our experimental results show that with more clusters, the number of anomalies detected tends to decrease for the clustering models considered. This means false alarms can be reduced by increasing the number of clusters used.</p>en
dc.languageenen
dc.publisherIEEEen
dc.relation.ispartof1st International Conference on Computer and Drone Applications: Ethical Integration of Computer and Drone Technology for Humanity Sustainability, IConDA 2017en
dc.titleInvestigating the effects of varying cluster numbers on anomalies detected in mining machinesen
dc.typeConference Publicationen
dc.relation.conferenceIConDA 2017: 1st International Conference on Computer and Drone Applicationsen
dc.identifier.doi10.1109/ICONDA.2017.8270404en
local.contributor.firstnameZongwenen
local.contributor.firstnameRaymonden
local.contributor.firstnameZhongyien
local.contributor.firstnameYuqingen
local.profile.schoolSchool of Science & Technologyen
local.profile.emailrchiong@une.edu.auen
local.output.categoryE1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.date.conference9th - 11th November, 2017en
local.conference.placeKuching, Malaysiaen
local.publisher.placeUnited States of Americaen
local.format.startpage82en
local.format.endpage86en
local.peerreviewedYesen
local.contributor.lastnameFanen
local.contributor.lastnameChiongen
local.contributor.lastnameHuen
local.contributor.lastnameLinen
dc.identifier.staffune-id:rchiongen
local.profile.orcid0000-0002-8285-1903en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:1959.11/61459en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleInvestigating the effects of varying cluster numbers on anomalies detected in mining machinesen
local.output.categorydescriptionE1 Refereed Scholarly Conference Publicationen
local.conference.detailsIConDA 2017: 1st International Conference on Computer and Drone Applications, Kuching, Malaysia, 9th - 11th November, 2017en
local.search.authorFan, Zongwenen
local.search.authorChiong, Raymonden
local.search.authorHu, Zhongyien
local.search.authorLin, Yuqingen
local.uneassociationNoen
local.atsiresearchNoen
local.sensitive.culturalNoen
local.year.published2017en
local.year.presented2017en
local.subject.for20204602 Artificial intelligenceen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.date.moved2024-08-29en
Appears in Collections:Conference Publication
School of Science and Technology
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