Buried Pipeline Wall-Loss Detection With Contactless EM Sensing
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Solution Overview
Problem
Existing pipeline inspection technologies face challenges in accurately and efficiently detecting wall-loss conditions due to corrosion, particularly pinholes, with high false positive and negative rates, and require lengthy and costly processes.
Innovation Solution
Integration of AI-powered analytics with electromagnetic (EM) based sensing modules into pipeline intervention gadgets (PIGs) for contactless detection, utilizing artificial neural networks (ANNs) trained on simulated and field data to predict wall-loss conditions, enabling near real-time reporting and reduced operational costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional pipeline inspection technologies are used, then detection processes can be performed, but accuracy in detecting wall-loss conditions is low with high false positive and negative rates
Solution Approach 1:
The patent replaces traditional mechanical contact-based inspection methods with electromagnetic sensing modules that detect wall-loss conditions through contactless electromagnetic measurements, thereby improving detection accuracy and reducing false positives and negatives
Solution Approach 2:
The patent transforms raw electromagnetic sensor data into meaningful defect predictions by changing the parameter representation through AI/ML model processing, converting electrical signals into diagnostic information about pipeline wall conditions
2Productivity
If traditional pipeline inspection processes are used, then inspection can be performed, but the process is lengthy and costly
Solution Approach 1:
The patent replaces lengthy manual inspection processes with automated electromagnetic sensing and AI-based analysis, enabling rapid detection and significantly reducing the time required for pipeline inspection while maintaining high accuracy
Solution Approach 2:
The patent implements self-diagnostic capabilities where the pipeline inspection system automatically analyzes its own sensor data through embedded AI/ML models, eliminating the need for extensive manual analysis and reducing inspection time
3Measurement precision
If AI-powered analytics with EM sensing modules are used, then detection accuracy improves, but device complexity increases
Solution Approach 1:
The patent integrates multiple functions (electromagnetic sensing, AI analysis, defect prediction, and cleaning) into a single multi-functional pipeline inspection device, improving detection accuracy while managing complexity through functional integration
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Achieves higher accuracy in detecting pipeline anomalies with lower costs, providing dual functionality for cleaning and inspection, and near real-time reporting, reducing reliance on direct field data collection.
Implementation Method 1
the transmitter sends a known electromagnetic (EM) waveform inside the metal wall of the buried pipeline
Data Source
Figure 1A~1B
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AI summary
The present disclosure provides a method including: generating a definition of a buried pipeline and a tool, wherein the buried pipeline comprises a metal wall, wherein the tool comprises a transmitter and multiple receivers circumferentially positioned inside the metal wall but without contacting the metal wall; obtaining a solver configured to simulate a response on each of the multiple receivers; applying the solver based on, at least in part, the definition of the buried pipeline and the tool when the transmitter sends a known electromagnetic (EM) waveform; generating simulated responses on the multiple receivers from interacting with the wall of the buried pipeline; and based on, at least in part, the simulated responses, training an inference model configured to predict the wall-loss condition of a particular buried pipeline when presented with measurement data inside the particular buried pipeline.