Cargando…
An Intelligence Method for Recognizing Multiple Defects in Rail
Ultrasonic guided waves are sensitive to many different types of defects and have been studied for defect recognition in rail. However, most fault recognition algorithms need to extract features from the time domain, frequency domain, or time-frequency domain based on experience or professional know...
Autores principales: | Deng, Fei, Li, Shu-Qing, Zhang, Xi-Ran, Zhao, Lin, Huang, Ji-Bing, Zhou, Cheng |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8662418/ https://www.ncbi.nlm.nih.gov/pubmed/34884112 http://dx.doi.org/10.3390/s21238108 |
Ejemplares similares
-
Detection of Rail Defects Using NDT Methods
por: Xiong, Longhui, et al.
Publicado: (2023) -
On the Use of Microwave Holography to Detect Surface Defects of Rails and Measure the Rail Profile
por: Zhuravlev, Andrey, et al.
Publicado: (2019) -
A Defect Detection Method for Rail Surface and Fasteners Based on Deep Convolutional Neural Network
por: Zheng, Danyang, et al.
Publicado: (2021) -
Ensemble model for rail surface defects detection
por: Li, Hailang, et al.
Publicado: (2022) -
Intelligent metasurface imager and recognizer
por: Li, Lianlin, et al.
Publicado: (2019)