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Minimizing profile error when estimating the sieve-size distribution of iron ore pellets using ordinal logistic regression
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.ORCID-id: 0000-0001-6186-7116
2011 (engelsk)Inngår i: Powder Technology, ISSN 0032-5910, E-ISSN 1873-328X, Vol. 206, nr 3, s. 218-226Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Size measurement of pellets in industry is usually performed by manual sampling and sieving techniques. Automatic on-line analysis of pellet size based on image analysis techniques would allow non-invasive, frequent and consistent measurement. We evaluate the statistical significance of the ability of commonly used size and shape measurement methods to discriminate among different sieve-size classes using multivariate techniques. Literature review indicates that earlier works did not perform this analysis and selected a sizing method without evaluating its statistical significance. Backward elimination and forward selection of features are used to select two feature sets that are statistically significant for discriminating among different sieve-size classes of pellets. The diameter of a circle of equivalent area is shown to be the most effective feature based on the forward selection strategy, but an unexpected five-feature classifier is the result using the backward elimination strategy. The discrepancy between the two selected feature sets can be explained by how the selection procedures calculate a feature's significance and that the property of the 3D data provides an orientational bias that favours combination of Feret-box measurements. Size estimates of the surface of a pellet pile using the two feature sets show that the estimated sieve-size distribution follows the known sieve-size distribution.

sted, utgiver, år, opplag, sider
2011. Vol. 206, nr 3, s. 218-226
HSV kategori
Forskningsprogram
Signalbehandling; Industriell elektronik
Identifikatorer
URN: urn:nbn:se:ltu:diva-11427DOI: 10.1016/j.powtec.2010.09.021ISI: 000286299200003Scopus ID: 2-s2.0-78649714204Lokal ID: a6330560-c6eb-11df-a707-000ea68e967bOAI: oai:DiVA.org:ltu-11427DiVA, id: diva2:984377
Prosjekter
Vision Systems Research Platform
Merknad
Validerad; 2011; 20100923 (tobiasa)Tilgjengelig fra: 2016-09-29 Laget: 2016-09-29 Sist oppdatert: 2018-07-10bibliografisk kontrollert

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Andersson, TobiasThurley, Matthew

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