Optimized Gaussian Process Regression for Prediction of Oil and Gas Pipelines Defect Length

Magnetic flux leakage (MFL) signals are used to estimate the scale and form of faults caused by the decaying metal used to build oil and gas pipelines. These faults, such as rust, can have catastrophic consequences if left undetected and improperly treated, both in terms of environmental damage and loss of life, as well as millions of dollars in maintenance costs for the stakeholders. Machine learning algorithms have proven their ability to solve the problem by correctly recognizing and calculating the scale and form of certain defects. The nonparametric and Bayesian approach to regression known as Gaussian process regression (GPR) is gaining popularity in machine learning. The optimization of GPR was carried out in this report using noisy and noiseless MFL signal measurements. The tune-able hyper-parameters were subjected to GPR optimization. Root mean square error (RMSE) error was used to calculate the output. In this research, the Quasi-Newton Method (QNM), an automated methodology for optimizing nonparametric regression analysis, was used to refine the GPR model. The optimization results are then compared to GPR analysis with default parameters, and it has been shown that QNM effectively optimizes the GPR while producing lower RMSE scores on all datasets. The ideal inferred parameter set can be used to train the GPR model for better output outcomes in determining oil and gas pipeline defects.