Predictive modeling for shrink sleeve failure using machine learning

摘要

In 2007, external corrosion and linear indications were discovered underneath shrink sleeves installed on field girth welds located on a 10-mile section of natural gas transmission pipeline constructed in 1993.Over the next 10 years, 1,615 of 1,971 shrink sleeves were removed.Given the volume of this dataset, the operator established a goal to prioritize the removal of the remaining shrink sleeves in this segment, while establishing a predictive proactive model, to prioritize shrink sleeve removal across the system.The system is expected to contain nearly 20,000 shrink sleeves.To build this model, data was managed in a custom developed SQL database with Microsoft PowerBI, ESRI ArcGIS Server Portals, and machine learning algorithms applied for powerful analytics.We will define the lifecycle of a shrink sleeve to be four stages: 1.Stage 1: Intact;2.Stage 2: Failed(i.e.disbonded)-no corrosion;3.Stage 3: Failed-with corrosion;4.Stage 4: Failed-with corrosion and cracking.In concept, Stages 3 and 4 can be detected through In-Line Inspection(ILI), whereas Stages 1 and 2 are believed to be predictable through modelling.The fundamental result of this project delivered a data structure where advanced predictive analytics can be applied to develop models capable of predicting Stage 2, specifically.