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.

中文翻译：

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基于在线检测数据分析的管道腐蚀缺陷分析与预测

在线检查 (ILI) 对于管道完整性管理很重要，因为它可以检测管道缺陷并通过定期检查识别潜在的故障位置。然而，基于 ILI 数据有效评估缺陷具有挑战性。ILI 的测量很容易受到仪器性能和维护活动的影响，从而导致不匹配和不平衡的数据。ILI 数据不佳导致难以建立基于多次检测的缺陷增长模型。本研究对 ILI 数据进行了综合分析，以评估钢管的腐蚀缺陷。首先，对原始数据进行统计分析，以可视化腐蚀深度和腐蚀次数的分布。第二，采用层次聚类方法，根据腐蚀深度特征和预估修复因子对腐蚀严重程度进行分类。考虑了相邻腐蚀之间的相互作用。机器学习方法，包括 k 最近邻、支持向量机、随机森林和光梯度增强机被用来探索相邻腐蚀的位置参数与严重程度之间的关系。然后，从多次检测的原始 ILI 数据中过滤出最大腐蚀深度和腐蚀密度，这对管道故障预测至关重要。最后，拟合分布参数建立最大腐蚀深度和腐蚀数密度的随机增长模型。