Health status identification of catenary based on VMD and FA-ELM

Catenary works as a key part in the electric railway traction power supply system, which is exposed outdoors for a long time and the failure rate is very high. Once a failure occurs, it will directly affect the driving safety. Based on the above, a model of identifying the health status for the cate...

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Bibliographic Details
Published in:Journal of algorithms & computational technology 2021-07, Vol.15
Main Authors: Yi, Lingzhi, Guo, You, Liu, Nian, Zhao, Jian, Li, Wang, Sun, Junyong
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Correlation coefficients
Decomposition
Electric railroads
Failure rates
Heuristic methods
Machine learning
Vehicle safety
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Summary:Catenary works as a key part in the electric railway traction power supply system, which is exposed outdoors for a long time and the failure rate is very high. Once a failure occurs, it will directly affect the driving safety. Based on the above, a model of identifying the health status for the catenary based on firefly algorithm optimized extreme learning machine combined with variational mode decomposition is proposed in this paper. Variational mode decomposition is used to decompose the original detection curve of catenary into a series of intrinsic mode function components, and the intrinsic mode function components filtered by the correlation coefficient method after decomposing each detection curve are input into the firefly algorithm optimized extreme learning machine model to realize health status identification. Compared with some other models, the results show that the proposed model has better health status identification effect.
ISSN:1748-3018
1748-3026
DOI:10.1177/17483026211024898