MATLAB IMPLEMENTATION OF MULTILAYER PERCEPTRON FOR BEARING FAULTS CLASSIFICATION

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F21%3APU140813" target="_blank" >RIV/00216305:26220/21:PU140813 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
MATLAB IMPLEMENTATION OF MULTILAYER PERCEPTRON FOR BEARING FAULTS CLASSIFICATION
Original language description
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.
Czech name
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Czech description
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Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings II of the 27th Conference STUDENT EEICT 2021
ISBN
978-80-214-5943-4
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
1-5
Publisher name
Brno University of Technology, Faculty of Electrical Engineering and Communication
Place of publication
Brno
Event location
Brno
Event date
Apr 27, 2021
Type of event by nationality
CST - Celostátní akce
UT code for WoS article
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