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Data-Driven Machine Learning Techniques Case Study for Corrosion Depth Prediction in Facilities Station Piping

Product Number: 51323-18927-SG

Author: Kane Kaiqi Cheng, Michael Snow

Publication Date: 2023

$20.00

The pipeline industry has widely used integrity principles to manage time-dependent and time-independent threats. The detection of time-dependent threats such as corrosion has been accomplished by using inline inspection tool technologies such as ultrasonic and magnetic flux leak inspection tools. However, most facility piping assets can not easily be inspected using in-line inspection methods and must instead be assessed using data collected from operations, such as flow frequency, product type, Cathodic protection record, or Direct Assessment Methods using Non Destructive Testing such as ultrasonic measurements or monitoring of corrosion coupons.

Products tagged with 'station piping'

Data-Driven Machine Learning Techniques Case Study for Corrosion Depth Prediction in Facilities Station Piping

Association for Materials Protection and Performance