Ultrasonic Guided Waves and Machine Learning for Corrosion Monitoring in Steel Pipes

In a pipe, a circumferentially travelling ultrasonic wave will gather information about the properties and boundaries of the propagation medium. However, the compounded effects of diagnostic features like uniform pipe wall thinning, surface roughness, regional depressions, and pit developments are difficult to separate using traditional methods. Therefore, this study proposes an approach using artificial neural networks to estimate the diagnostic features of interest. This study is based on ultrasound simulations and synthetic data. The synthetic data is recorded at a set of transducer positions at the outer pipe wall. The resulting traces are then combined into 2D images where each vertical line represents a trace and thus a transducer location. The resulting images are used to train a neural network to extract relevant features. Diagnostic features for mean and minimum thickness, as well as roughness, are quite accurately estimated. Features for depth and location of depressions and depth of pits are also informative but less accurate.