Searched for: subject:"Degradation"
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document
de Bruin, T.D. (author), Verbert, K.A.J. (author), Babuska, R. (author)
Timely detection and identification of faults in railway track circuits are crucial for the safety and availability of railway networks. In this paper, the use of the long-short-term memory (LSTM) recurrent neural network is proposed to accomplish these tasks based on the commonly available measurement signals. By considering the signals from...
journal article 2017
document
Faghih Roohi, S. (author), Hajizadeh, S. (author), Nunez Vicencio, A.A. (author), Babuska, R. (author), De Schutter, B.H.K. (author)
In this paper, we propose a deep convolutional neural network solution to the analysis of image data for the detection of rail surface defects. The images are obtained from many hours of automated video recordings. This huge amount of data makes it impossible to manually inspect the images and detect rail surface defects. Therefore, automated...
conference paper 2016