Accurate Identification Partial Discharge of Cable Termination for High-Speed Trains Based on S-Transform and Two-Dimensional Convolutional Network Algorithm
Yunlong Xie, Peng You, Guangning Wu, Tingyu Zhang, Yang Luo, Shuyuan Zhou, Kai Liu, Kui Chen, Dongli Xin, Guoqiang Gao, Pavlos Lazaridis · 2024
Read Accurate Identification Partial Discharge of Cable Termination for High-Speed Trains Based on S-Transform and Two-Dimensional Convolutional Network Algorithm by Yunlong Xie, Peng You, Guangning Wu, Tingyu Zhang, Yang Luo, Shuyuan Zhou, Kai Liu, Kui Chen, Dongli Xin, Guoqiang Gao, Pavlos Lazaridis (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11644701.txt.
Description
Yunlong Xie and co-authors present a method in 2024 to spot insulation faults in high-speed train cables from faint discharge signals. Cable ends are a weak point, and true fault signals get mixed with corona noise in field tests. Their model pairs a time-frequency transform with a two-dimensional neural network. A wavelet step cleans the signal first.
The system sorted fault signals from corona noise with accuracy near ninety-nine percent. A trick that centers the time window on the strongest moment fixed a common error with long signals. The method should help crews judge cable health more reliably. Safer, better-timed maintenance for train fleets is the goal.
Catalog record
| CREATOR | Yunlong Xie, Peng You, Guangning Wu, Tingyu Zhang, Yang Luo, Shuyuan Zhou, Kai Liu, Kui Chen, Dongli Xin, Guoqiang Gao, Pavlos Lazaridis |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 34 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |