Using Machine Learning To Detect Railway Defects

Using Machine Learning To Detect Railway Defects

The article describes the experience of effectively using machine learning to automatically detect defects during ultrasonic control of railway rails.

The article describes the successful experience of using machine learning (artificial intelligence) to automatically detect defects during ultrasonic control of railway rails. An effective method of creating a training dataset for a neural network using mathematical modeling is presented, which made it possible to achieve high accuracy indicators for recognizing real defectograms. There given examples of prototypes of trained neural network operation with achieved prediction accuracy of real defectograms equal to 92%.

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