Analysis of Neural Network Training Algorithms for Implementation of the Prescriptive Maintenance Strategy

Analysis of Neural Network Training Algorithms for Implementation of the Prescriptive Maintenance Strategy

LEMPA Paweł and FILO Grzegorz

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Abstract. This paper presents a proposal to combine supervised and semi-supervised training strategies to obtain a neural network for use in the prescriptive maintenance approach. It is required in this approach because of only partially labelled data for use in supervised learning, and additionally, this data is predicted to expand quickly. The main issue is the decision on which are suitable training methodologies for supervised learning, having in mind using this data and methods for semi-supervised learning. The proposed methods of training neural networks with supervised and semi-supervised training to receive the best results will be tested and compared in further work.

Neural Network Training, Multilayer Network Training, Supervised Training, Semi-Supervised Training, Prescriptive Maintenance

Published online 7/20/2022, 7 pages
Copyright © 2022 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: LEMPA Paweł and FILO Grzegorz, Analysis of Neural Network Training Algorithms for Implementation of the Prescriptive Maintenance Strategy, Materials Research Proceedings, Vol. 24, pp 281-287, 2022


The article was published as article 41 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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