Robotic Pipe Corrosion Inspection Guided by Metal Loss Prediction
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Solution Overview
Problem
The oil and gas industry faces challenges in efficiently detecting and remediating localized metal loss in infrastructure due to the time-consuming and resource-intensive nature of manual inspections, with existing automated techniques often failing to accurately predict high-risk areas for corrosion.
Innovation Solution
A method utilizing a machine learning model trained on historical data to predict metal loss in pipe structures, coupled with a robotic vehicle for confirmation and remediation, which includes deploying a robotic vehicle equipped with inspection and remediation modules to target high-risk areas for precise inspection and repair.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection methods are used to detect corrosion, then inspection accuracy can be maintained through direct observation, but the process becomes time-consuming and requires structures to be placed offline
Solution Approach 1:
The patent replaces manual mechanical inspection with automated non-invasive techniques including infrared thermal imaging devices mounted on robotic vehicles. This substitution maintains detection capability while eliminating the need for personnel to physically access and manually inspect structures, thereby reducing inspection time without sacrificing accuracy
Solution Approach 2:
The patent introduces infrared thermal imaging as an intermediary tool that detects corrosion indirectly through thermal patterns rather than direct visual observation. This intermediary method enables automated detection while maintaining the precision needed to identify corrosion issues without requiring offline placement of structures
2Productivity
If automated non-invasive techniques are used to detect corrosion, then inspection speed and coverage are improved, but the ability to accurately predict high-risk areas for corrosion is insufficient
Solution Approach 1:
The patent applies preliminary action by using machine learning models trained on historical corrosion data to predict and identify high-risk areas before conducting detailed inspections. This preliminary prediction step enables the system to focus automated inspection resources on areas most likely to have corrosion, improving both efficiency and prediction accuracy
Solution Approach 2:
The patent implements feedback mechanisms where inspection results are fed back into the machine learning model to continuously improve prediction accuracy. The system learns from actual corrosion findings and adjusts its predictions, creating a closed-loop system that enhances both productivity and measurement precision over time
3Ease of operation
If periodic manual inspections are conducted, then resource allocation can be optimized by focusing on high-risk areas, but the predetermination of high-risk structures is subject to error
Solution Approach 1:
The patent enables the system to self-service by automatically identifying high-risk areas through machine learning algorithms without relying on manual expert judgment. The system autonomously analyzes data, predicts corrosion risks, and allocates inspection resources, eliminating human error in high-risk area identification while maintaining operational efficiency
Solution Approach 2:
The patent changes the parameters used for identifying high-risk areas from subjective expert assessment to objective data-driven metrics including historical corrosion rates, environmental conditions, and structural characteristics. This parameter transformation improves both the reliability of high-risk identification and the efficiency of resource allocation
4Measurement precision
If comprehensive inspection of all structures is performed, then corrosion detection coverage is maximized, but the time and resources required increase significantly
Solution Approach 1:
The patent applies local quality by concentrating inspection resources on specific high-risk locations identified through machine learning predictions rather than uniformly inspecting all structures. This localized approach maintains comprehensive coverage of critical areas while improving overall inspection throughput by avoiding unnecessary inspections of low-risk structures
Data Source
AI summary
A method according to the disclosure configures a processor to predict metal loss in a structure for remediation. The method uses a machine learning model, trained based upon historical data, to predict metal loss over locations of a structure at a time of the prediction. The method identifies from among the predicted locations a high-risk location on the structure in which a magnitude of metal loss indicates potential remediation being needed, dispatches a robotic vehicle to the high-risk location on the structure and inspects the high-risk location using the robotic vehicle to confirm whether the magnitude of metal loss at the location requires remediation. In further methods, remediation is performed. In still further methods, a three-dimensional visualization of the structure is generated with an overlay which depicts predicted metal loss over the sections of the structure.