A method of predicting wear and damage of pantograph sliding strips based on artificial neural networks
Authors:
- Małgorzata Kuźnar,
- Augustyn Lorenc
Abstract
The impact of the pantograph of a rail vehicle on the overhead contact line depends on many factors. Among other things, the type of pantograph, i.e., the material of the sliding strip, influences the wear and possible damage to the sliding strip. The possibility of predicting pantograph failures may make it possible to reduce the number of these kinds of failures. This article presents a method for predicting the technical state of the pantograph by using artificial neural networks. The presented method enables the prediction of the wear and damage of the pantograph, with particular emphasis on carbon sliding strips. The paper compares 12 predictive models based on regression algorithms, where different training algorithms and activation functions were used. Two different types of training data were also used. Such a distinction made it possible to determine the optimal structure of the input and output data teaching the neural network, as well as the determination of the best structure and parameters of the model enabling the prediction of the technical condition of the current collector.
- Record ID
- CUTc1bd24ca8f06467b97978377575c40f9
- Publication categories
- ;
- Author
- Journal series
- Materials, ISSN , e-ISSN 1996-1944, Biweekly
- Issue year
- 2022
- Vol
- 15
- No
- 1
- Pages
- [1-23]
- Article number
- 98
- Other elements of collation
- schem.; tab.; wykr.; Bibliografia (na s.) - 21-23; Bibliografia (liczba pozycji) - 51; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 15, Iss. 1, Spec. Iss.
- Substantive notes
- Special Issue: Applied Engineering Materials: Development, Characterization, Statistical Analysis, Simulation, and Soft Computing
- Keywords in English
- pantograph sliding strip, AI for prediction, artificial neural network, damage prevention, predictive maintenance
- ASJC Classification
- DOI
- DOI:10.3390/ma15010098 Opening in a new tab
- URL
- https://www.mdpi.com/1996-1944/15/1/98 Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 140
- Score source
- journalList
- Score
- Publication indicators
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUTc1bd24ca8f06467b97978377575c40f9/
- URN
urn:pkr-prod:CUTc1bd24ca8f06467b97978377575c40f9
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.