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DOSEDĚL, M.
Original Title
MATLAB IMPLEMENTATION OF MULTILAYER PERCEPTRON FOR BEARING FAULTS CLASSIFICATION
Type
conference paper
Language
English
Original Abstract
This paper deals with implementation of multilayer perceptron neural network (NN) for bearing faults classification. Neural network has been created from scratch as an M-script with back propagation learning algorithm also, but without using advanced MATLAB packages. Public available bearing dataset from CaseWestern Reserve University has been used for both training and testing phase, as well as for the final classification process. Problem with sparse input data for training the network has also been addressed. This relatively simple and small neural network is capable to classify the failures of a bearing with very low error rate.
Keywords
Multilayer perceptron (MLP), deep learning, data classification, back-propagation algorithm, bearing faults
Authors
Released
27. 4. 2021
Publisher
Brno University of Technology, Faculty of Electrical Engineering and Communication
Location
Brno
ISBN
978-80-214-5943-4
Book
Proceedings II of the 27th Conference STUDENT EEICT 2021 selected papers
Edition
1
Pages from
161
Pages to
165
Pages count
5
URL
https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2021_sbornik_2_v3_DOI.pdf
BibTex
@inproceedings{BUT171497, author="Martin {Doseděl}", title="MATLAB IMPLEMENTATION OF MULTILAYER PERCEPTRON FOR BEARING FAULTS CLASSIFICATION", booktitle="Proceedings II of the 27th Conference STUDENT EEICT 2021 selected papers", year="2021", series="1", pages="161--165", publisher="Brno University of Technology, Faculty of Electrical Engineering and Communication", address="Brno", doi="10.13164/eeict.2021.161", isbn="978-80-214-5943-4", url="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2021_sbornik_2_v3_DOI.pdf" }