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Remaining useful life prediction of grinding mill liners using an artificial neural network
Luleå University of Technology, Luleå, Sweden .ORCID iD: 0000-0002-8524-3321
Luleå University of Technology, Luleå, Sweden .
2013 (English)In: Minerals Engineering, ISSN 0892-6875, E-ISSN 1872-9444, Vol. 53, p. 1-8Article in journal (Refereed) Published
Abstract [en]

Knowing the remaining useful life of grinding mill liners would greatly facilitate maintenance decisions. Now, a mill must be stopped periodically so that the maintenance engineer can enter, measure the liners' wear, and make the appropriate maintenance decision. As mill stoppage leads to heavy production losses, the main aim of this study is to develop a method which predicts the remaining useful life of the liners, without needing to stop the mill. Because of the proven ability of artificial neural networks (ANNs) to recognize complex relationships between input and output variables, as well as its adaptive and parallel information-processing structure, an ANN has been designed based on the various process parameters which influence wear of the liners. The process parameters were considered as inputs while remaining height and remaining life of the liners were outputs. The results show remarkably high degree of correlation between the input and output variables. The performance of the neural network model is very consistent for data used for training (seen) and testing (unseen).

Place, publisher, year, edition, pages
2013. Vol. 53, p. 1-8
National Category
Other Engineering and Technologies
Identifiers
URN: urn:nbn:se:mdh:diva-26428DOI: 10.1016/j.mineng.2013.05.026ISI: 000328522900001Scopus ID: 2-s2.0-84881297898OAI: oai:DiVA.org:mdh-26428DiVA, id: diva2:760341
Available from: 2014-11-03 Created: 2014-10-31 Last updated: 2017-12-05Bibliographically approved

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Ahmadzadeh, Farzaneh

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