Matlab Implementation Of Multilayer Perceptron For Bearing Faults Classification

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161_EEICT_2021_2.pdf(1.01 MB)
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2021
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Mark
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Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
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Abstract
This paper deals with implementation of multilayer perceptron neural network (NN) forbearing faults classification. Neural network has been created from scratch as an M-script with backpropagation learning algorithm also, but without using advanced MATLAB packages. Public availablebearing dataset from CaseWestern Reserve University has been used for both training and testingphase, as well as for the final classification process. Problem with sparse input data for training thenetwork has also been addressed. This relatively simple and small neural network is capable to classifythe failures of a bearing with very low error rate.
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Multilayer perceptron (MLP), deep learning, data classification, back-propagation algorithm,bearing faults
Citation
Proceedings II of the 27st Conference STUDENT EEICT 2021: Selected Papers. s. 161-165. ISBN 978-80-214-5943-4
https://conf.feec.vutbr.cz/eeict/index/pages/view/ke_stazeni
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Peer-reviewed
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en
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© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
DOI
10.13164/eeict.2021.161
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http://hdl.handle.net/11012/200833
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Student EEICT 2021: Selected Papers
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