Multi-Modal ILI Data Fusion for Combined Diagnostics of Pipeline

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Metal loss and hidden flaws are critical threats to the pipeline for transporting oil and gas products. Non-destructive testing (NDT) techniques are employed as the in-line inspection (ILI) to evaluate and maintain the structural integrity during the pipeline's entire lifecycle. However, due to the limitation of the individual NDT technique, multiple NDT methods are often applied, and a comprehensive interpretation of the inspection results from the combination will achieve a precise integrity analysis. ROSEN's magnetic flux leakage (MFL) and ultrasonic wall measurement (UTWM) inspection tools are designed to apply high-level magnetization in combination with high-power ultrasonic waves to the pipeline. This combination of two NDT applications combines the best of the two techniques to ensure the pipeline's structural integrity during its entire lifespan. However, an automated process to interpret and analyze the ILI data is critical for accurate and reliable condition assessment. This research project is to develop analytic algorithms to fuse the MFL and UTWM data for the combined diagnosis of pipeline integrity issues. The research will first focus on the uncertainty analysis of the ILI data. Then, we will convert ILI data from one domain to another with deep learning approaches, for example, translating MFL data to UTMW data and vice versa. With the knowledge of the uncertainty, the translated ILI data can be aligned or registered based on the salient features and fused with the autoencoder network. The outcomes of the research will be integrated into ROSEN's RoCombo MFL/UTWM inspection system for improved characterization of interacting defects. Thus, the comprehensive inspection with data analytics can achieve reliable results to support the decision-making for pipeline management.