Health status identification of catenary based on VMD and FA-ELM. (July 2021)

• Record Type: Journal Article
• Authors: Yi, Lingzhi; Guo, You; Liu, Nian; Zhao, Jian; Li, Wang; Sun, Junyong
• Is Part Of: Journal of algorithms & computational technology. Volume 15(2021)
• Publication Date: 2021-07
• Subjects: Catenary -- firefly algorithm -- variational mode decomposition -- extreme learning machine -- health status identification; Computer algorithms -- Periodicals; Numerical calculations -- Periodicals; Computer algorithms; Numerical calculations; Periodicals; 518.1
• DOI: 10.1177/17483026211024898
• Languages: English
• ISSNs: 1748-3018

Abstract

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.