Open AccessFault diagnosis of roller bearings using selected classifiers
Author(s) -
Jakub Piekoszewski
Publication year - 2018
Publication title -
autobusy
Language(s) - English
Resource type - Journals
eISSN - 2450-7725
pISSN - 1509-5878
DOI - 10.24136/atest.2018.460
Subject(s) - artificial neural network , computer science , artificial intelligence , field (mathematics) , decision tree , naive bayes classifier , bearing (navigation) , perceptron , multilayer perceptron , machine learning , fault (geology) , data mining , software , support vector machine , pattern recognition (psychology) , mathematics , seismology , pure mathematics , programming language , geology
Minor roller bearing damage may lead to serious failures of the de-vice. Thus, it is very important to detect such damage as early as possible to prevent further damage. This paper presents a selection of several theoretical tools from the field of artificial intelligence and their application in roller bearings fault classification. The considered tools are: k-nearest neighbour algorithm, decision tree, support vector machine, feed forward neural network (multilayer perceptron), Bayesian network and neural network with radial basis functions. All numerical experiments presented in the paper were performed with the use of real-world dataset and WEKA (Waikato Environment for Knowledge Analysis) software, available at the server of the University of Waikato.