Convolutional Autoencoder-Based Damage Detection for Urban Railway Tracks Using an Ultra-Weak FBG Array Monitoring System

Urban railway tracks inevitably suffer damage due to the cyclic load of trains, necessitating structural health monitoring (SHM) with the primary purpose of damage detection. Recently, the ultra-weak fiber Bragg grating (UWFBG) monitoring system has been employed for long-term monitoring of urban ra...

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Vydané v:	IEEE sensors journal Ročník 24; číslo 20; s. 33585 - 33593
Hlavní autori:	Chen, Jiahui, Li, Qiuyi, Zhang, Shijie, Lin, Chao, Wei, Shiyin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 15.10.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Artificial neural networks Autocorrelation functions Bragg gratings Cyclic loads Damage detection Data mining Data models deep convolutional autoencoder (DCAE) Error detection Feature extraction Machine learning Monitoring Monitoring systems Optical fibers Rail transportation Railway engineering Railway tracks Reconstruction Sensors Structural health monitoring Trains ultra-weak fiber Bragg grating (UWFBG) Unsupervised learning urban railway track vibration monitoring Vibrations
ISSN:	1530-437X, 1558-1748
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