Analysis of Neural Network Structure for Implementation of the Prescriptive Maintenance Strategy
FILO Grzegorz and LEMPA Paweł
download PDFAbstract. This paper provides an initial analysis of neural network implementation possibilities in practical implementations of the prescriptive maintenance strategy. The main issues covered are the preparation and processing of input data, the choice of artificial neural network architecture and the models of neurons used in each layer. The methods of categorisation and normalisation within each distinguished category were proposed in input data. Based on the normalisation results, it was suggested to use specific neuron activation functions. As part of the network structure, the applied solutions were analysed, including the number of neuron layers used and the number of neurons in each layer. In further work, the proposed structures of neural networks may undergo a process of supervised or partially supervised training to verify the accuracy and confidence level of the results they generate.
Keywords
Artificial Neural Network, Neuron Model, Layer Model, Prescriptive Maintenance, Input Signal Normalisation
Published online 7/20/2022, 8 pages
Copyright © 2022 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: FILO Grzegorz and LEMPA Paweł, Analysis of Neural Network Structure for Implementation of the Prescriptive Maintenance Strategy, Materials Research Proceedings, Vol. 24, pp 273-280, 2022
DOI: https://doi.org/10.21741/9781644902059-40
The article was published as article 40 of the book Terotechnology XII
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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