Analysis and prediction of pipeline corrosion defects based on data analytics of in-line inspection

Abstract

In-line inspection (ILI) is important to pipeline integrity management since it can detect pipeline defects and identify potential failure locations through periodical examinations. However, effectively evaluating defects based on ILI data is challenging. Measurements of ILI are easily influenced by instrument performance and maintenance activities, leading to unmatched and imbalanced data. Poor ILI data make it difficult to establish defect growth models based on multiple inspections. This study conducted comprehensive analysis of ILI data for evaluating corrosion defects of a steel pipeline. First, statistical analysis was performed on raw data to visualize distributions of corrosion depths and number of corrosions. Second, hierarchical clustering method was used to classify corrosion severity levels based on features of corrosion depth and estimated repair factor. The interaction effect between adjacent corrosions was considered. Machine learning methods, including k-nearest neighbor, support vector machine, random forest, and light gradient boosting machine were used to explore the relationship between the location parameters of adjacent corrosions and severity levels. Then, maximum corrosion depths and corrosion density were filtered from raw ILI data of multiple inspections, which were critical for pipeline failure prediction. Finally, distribution parameters were fitted to establish stochastic growth models on maximum corrosion depth and corrosion number density. This study presents data analytics based approach to obtain valid information from ILI data in practice.