Selection of an algorithm for the prediction of stoppages and/or failure of excavation units using supervised machine learning-Reference-Cited by-同舟云学术

Selection of an algorithm for the prediction of stoppages and/or failure of excavation units using supervised machine learning

Published:2024 Issue: Volume: Page:79-91
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Container-title:XIX International May Conference on Strategic Management – IMCSM24 Proceedings - zbornik radova
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Author:

Misita Mirjana^ORCID,Spasojević-Brkić Vesna^ORCID,Mihajlović Ivan^ORCID,Brkić Aleksandar^ORCID,Perišić Martina^ORCID,Papić Neda^ORCID,Janev Nemanja^ORCID

Abstract

The paper presents research into the possibility of applying machine learning algorithms in the prediction of stoppages and/or failure of excavator units. Regression trees, Random Forest and Support Vector Machine (SVM) algorithms were tested with different hyperparameter variations on the collected set of data on the causes and downtime of stoppages of the observed excavator units. The result indicates that the trained SVM algorithm with sufficient accuracy (MSE 0.106) can predict the stoppages of the observed excavator units. Further research is aimed at expanding the database and further improving the possibility of predicting the level of danger for various causes of stoppages and/or failure of the observed excavator units.

Publisher

University of Belgrade, Technical Faculty in Bor

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