NEURAL NETWORKS

In this example, advanced mathematical solutions were built to automate the defectoscopy for a mid-stream company. By collecting different datasets for analysis from their pipeline assets, the model discovered defects as well as determined their type and size.

Neural networks and probabilistic trees were used to discover anomalies in magnetic flux leakage and ultrasonic sensor signals. By carefully “teaching” the system on test samples of defects with known sizes, the virtual sensing technology is now able to predict defect size and depth for industrial measurements in pipelines, allowing the system to quickly characterize defects as critical or non-critical.