Machine learning in problems of automation of ultrasound diagnostics of railway tracks
This work addresses railway maintenance automation for infrastructure managers, but appears incremental as it applies standard neural network methods to a specific domain.
The authors tackled the problem of automating ultrasound diagnostics for railway track defects by developing a real-time system architecture that includes data preprocessing and convolutional neural network classifiers, achieving effective implementation on modern parallel computing hardware.
The article presents the system architecture for automatic decoding of railway track defectograms in real time. The system includes an ultrasound data preprocessing module, a set of neutral network classifiers, a decision block. Preprocessing of data includes affine transformations of measurement information into a format suitable for the operation of a neural network, as well as a combination of information on measurement channels, depending on the type of defect being defined. The classifier is built on a convolutional neural network. The proposed solution can be effectively implemented on a modern elemental basis for performing parallel computing, including tensor processor and GPUs.