AI-Driven Smart Body for Contactless Pipeline Defect Detection
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Solution Overview
Problem
Existing pipeline inspection technologies struggle to accurately and efficiently detect defects, particularly in inaccessible pipelines, leading to high operational costs, false positives/negatives, and potential environmental and financial risks due to undetected corrosion.
Innovation Solution
A smart body equipped with electromagnetic emitters and receivers, powered by a buoyant housing and AI-driven processing, performs contactless scans using physics-driven machine learning to detect defects in pipelines, providing near real-time analysis and reducing reliance on field data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional pipeline inspection methods are used, then detection capability is limited, but operational costs increase and false positives/negatives occur
Solution Approach 1:
The patent replaces traditional mechanical contact-based inspection methods with electromagnetic sensing. The smart body uses electromagnetic transmitters and receivers to detect pipe wall defects without mechanical contact, eliminating the need for physical PIGs while improving detection accuracy and reducing operational costs.
Solution Approach 2:
The patent changes the detection parameter from mechanical measurement to electromagnetic signal analysis. By using electromagnetic fields to interact with the pipe wall and analyzing the induced signals, the system achieves higher precision in detecting corrosion and defects while improving operational efficiency.
2Adaptability or versatility
If contactless EM scanning is used in inaccessible pipelines, then detection capability improves, but device complexity increases
Solution Approach 1:
The smart body is designed as a universal inspection device that can operate in both accessible and inaccessible pipelines. It integrates electromagnetic transmitters, receivers, processors, and power supplies into a single multi-functional unit that adapts to different pipeline conditions without requiring complex specialized equipment.
Solution Approach 2:
The patent nests multiple functional components within the smart body structure. The electromagnetic transmitters and receivers are housed within the smart body, which itself is deployed within the pipeline. This nested arrangement allows the complex electromagnetic sensing system to operate in confined pipeline spaces.
3Measurement precision
If AI-driven real-time processing is implemented, then detection accuracy enhances, but energy consumption increases
Solution Approach 1:
The patent implements partial real-time processing by performing AI-driven analysis on the most critical signal features while deferring less critical computations. The smart body processes essential defect characteristics in real-time using low-power algorithms, while more intensive analysis can be performed offline, balancing accuracy with energy constraints.
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
The system achieves high detection rates with reduced operational costs, minimizes shutdown times, and enhances accuracy by integrating AI and EM-based sensing, enabling timely maintenance and reducing environmental impact.
Implementation Method 1
an electromagnetic (EM) transmitter surrounded by a plurality of EM receivers, the EM transmitter to transmit an alternating EM radiation signal through the buoyant housing, and the plurality of EM receivers to receive EM radiation through the buoyant housing, the received EM radiation to induce a signal in at least one of the plurality of EM receivers
Implementation Method 2
a buoyant housing that is buoyant in a production fluid
Data Source
AI summary
An apparatus includes a buoyant housing, a sensor arrangement housed within the buoyant housing that includes an electromagnetic (EM) transmitter surrounded by several EM receivers, a hardware processor, and instructions, including determining an induced signal value for each of the plurality of EM receivers; receiving the induced signal value for each of the plurality of EM receivers as an input for a neural network tangibly embodied on the non-transitory computer-readable storage medium; creating a visual representation of a body based on a neural network comparison of the induced signal values for each of the plurality of EM receivers; and determining whether a defect exists on the based on a comparison of the induced signal values for each of the plurality of EM receivers performed by the neural network.