The evaluation and interpretation of in-line inspection (ILI) data for pipeline crack detection remains a complex and resource-intensive challenge. Effective analysis requires not only extensive practitioner expertise but also robust algorithmic support to ensure efficient data preparation, variable selection, transformation, and classification. Given the high consequence of decisions based on these computational tools, it is essential to ensure that all algorithms—especially those leveraging artificial intelligence—are free from bias and strictly deployed within validated operational boundaries.
AI algorithms learn from annotated data instead of explicit instructions, which makes interpretability and generalization more complex. To guarantee reliable model performance across the broad spectrum of real-world ILI operational scenarios, it is imperative to develop and maintain comprehensive training and validation datasets. Additionally, because both field data and model validation data change over time, systematic lifecycle management practices are required to maintain model accuracy and reliability.

In this contribution, we illustrate our validation and lifecycle management strategy for algorithms and AI models by using a deep learning classification model for Electro-Magnetic-Acoustic-Transducer (EMAT) signal data for crack detection. Given that EMAT sensor data is heavily depending on specific pipeline parameters, it is necessary to manage multiple models tailored to distinct sets of essential variables, such as sensor type, pipe diameter, and wall thickness. Each of these models is validated using standardized performance objectives, consistent statistical analysis methods, and comprehensive documentation as demanded by industry standards such API 1163. Finally, we show how all models are regularly scheduled for re-assessments and updates when new data arrives.