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

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.

Product Number: 51323-18927-SG
Author: Kane Kaiqi Cheng, Michael Snow
Publication Date: 2023
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This study focuses on applying machine learning algorithms to predict the corrosion depth of facility station piping assets, as well as comparing the computational accuracy of the predicted corrosion depth based on various machine learning algorithms. Simulated corrosion testing data of facility piping was fit into the following machine learning algorithms: Gradient Boosting(GBM), Artificial Neural Network (ANN), and Random Forest (RF). K-fold cross validation was used to evaluate the models and grid search was applied for the models to refine and calibrate each model. The variable sensitivity analysis was conducted separately for the external and internal corrosion of station piping, and it assisted in limiting the number of independent variables included in machine learning models. This study compares the performance of corrosion depth prediction models for facility station piping and draws conclusions on model performance based on performance evaluation metrics.

This study focuses on applying machine learning algorithms to predict the corrosion depth of facility station piping assets, as well as comparing the computational accuracy of the predicted corrosion depth based on various machine learning algorithms. Simulated corrosion testing data of facility piping was fit into the following machine learning algorithms: Gradient Boosting(GBM), Artificial Neural Network (ANN), and Random Forest (RF). K-fold cross validation was used to evaluate the models and grid search was applied for the models to refine and calibrate each model. The variable sensitivity analysis was conducted separately for the external and internal corrosion of station piping, and it assisted in limiting the number of independent variables included in machine learning models. This study compares the performance of corrosion depth prediction models for facility station piping and draws conclusions on model performance based on performance evaluation metrics.