Publication detail

MATLAB IMPLEMENTATION OF MULTILAYER PERCEPTRON FOR BEARING FAULTS CLASSIFICATION

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

DOSEDĚL, M.

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"
}