[2B5] Enhancing pipeline structural health monitoring with AI-driven data analysis
K Sidor
Scope Inspection Ltd, UK
Pipeline structural health monitoring (SHM) is critical, yet conventional methods for assessing non-destructive testing (NDT) data are often manual and time intensive. This introduces inconsistencies and delays in decision-making, impacting the effective management of critical infrastructure.
This paper presents an artificial intelligence (AI)-driven system that automates the analysis of large volumes of technical documentation, including piping and instrumentation design (P&ID) diagrams and historical NDT reports, to improve the efficiency and reliability of integrity assessments. In a case study developed with TÜV Rheinland, the system was applied to automatically extract key inspection parameters and equipment metadata from unstructured reports.
The implementation led to a significant reduction in inspection reporting time, from several hours per asset to under 15 minutes. This accelerated risk-based inspection (RBI) workflows and improved the quality and consistency of data used for ongoing monitoring. Embedded validation models also identified errors in historical entries, aligning data with regulatory standards.
By structuring and validating disparate data sources, this machine learning approach provides a more robust foundation for SHM and predictive maintenance. This work demonstrates a practical application of NDE 4.0 principles, directly addressing the needs of NDT end-users for faster, more reliable integrity management.
This paper presents an artificial intelligence (AI)-driven system that automates the analysis of large volumes of technical documentation, including piping and instrumentation design (P&ID) diagrams and historical NDT reports, to improve the efficiency and reliability of integrity assessments. In a case study developed with TÜV Rheinland, the system was applied to automatically extract key inspection parameters and equipment metadata from unstructured reports.
The implementation led to a significant reduction in inspection reporting time, from several hours per asset to under 15 minutes. This accelerated risk-based inspection (RBI) workflows and improved the quality and consistency of data used for ongoing monitoring. Embedded validation models also identified errors in historical entries, aligning data with regulatory standards.
By structuring and validating disparate data sources, this machine learning approach provides a more robust foundation for SHM and predictive maintenance. This work demonstrates a practical application of NDE 4.0 principles, directly addressing the needs of NDT end-users for faster, more reliable integrity management.
