Detail publikace

MATLAB IMPLEMENTATION OF MULTILAYER PERCEPTRON FOR BEARING FAULTS CLASSIFICATION

DOSEDĚL, M.

Originální název

MATLAB IMPLEMENTATION OF MULTILAYER PERCEPTRON FOR BEARING FAULTS CLASSIFICATION

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

Multilayer perceptron (MLP), deep learning, data classification, back-propagation algorithm, bearing faults

Autoři

DOSEDĚL, M.

Vydáno

27. 4. 2021

Nakladatel

Brno University of Technology, Faculty of Electrical Engineering and Communication

Místo

Brno

ISBN

978-80-214-5943-4

Kniha

Proceedings II of the 27th Conference STUDENT EEICT 2021 selected papers

Edice

1

Strany od

161

Strany do

165

Strany počet

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"
}