CNN Pipeline Failure Categorization Using Image Recognition
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Solution Overview
Problem
The lack of automated systems for categorizing hydrocarbon transport pipeline failures leads to inefficient decision-making and potential neglect in pipeline maintenance, often due to the absence of subject matter experts with expertise in assessing these failures.
Innovation Solution
A computer-implemented method using image recognition and convolutional neural networks (CNNs) to categorize pipeline failures by analyzing images of pipeline segments, allowing for the creation of preliminary failure analysis reports without initial SME involvement, and continuously improving the accuracy of the system through feedback from experts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subject matter experts manually assess pipeline failures to categorize them, then the accuracy and reliability of failure categorization is improved, but the time consumption and operational efficiency deteriorate
Solution Approach 1:
The patent creates a digital copy of the expert assessment process by training a convolutional neural network on images of pipeline failures that have been categorized by subject matter experts. The CNN learns to replicate expert decision-making patterns, enabling automated categorization that mirrors human expert accuracy while eliminating time consumption. The system stores multiple pipeline images with expert-categorized failure modes in a database, allowing the AI to learn from these annotated examples and generate automated categorizations independently.
2Reliability
If subject matter experts are deployed to assess pipeline failures, then the quality of failure analysis reports is improved, but the cost and resource requirements deteriorate
Solution Approach 1:
The patent implements self-service by enabling the pipeline failure assessment system to automatically perform categorization without requiring continuous human expert intervention. The convolutional neural network, once trained on expert-categorized images, independently assesses new pipeline failure images, generates preliminary failure analysis reports, and maintains the image database. This automation eliminates the need for ongoing deployment of subject matter experts while preserving assessment quality, thereby reducing operational costs and resource requirements.
3Productivity
If automated systems are implemented for pipeline failure categorization, then productivity and speed are improved, but the initial complexity and development requirements worsen
Solution Approach 1:
The patent replaces the mechanical system of manual expert assessment with an automated computational system based on convolutional neural networks. Instead of relying on human experts to visually inspect and categorize pipeline failures, the system uses image processing algorithms that automatically analyze pipeline images, detect failure patterns, and categorize failures based on learned features from training data. This substitution dramatically increases productivity and assessment speed, as the AI system can process multiple images simultaneously without fatigue or delays associated with human work.
Data Source
AI summary
The present disclosure provides computer-implemented methods, media, and systems for automated hydrocarbon transport pipeline failure categorization using image recognition. One example method includes storing multiple pipeline images into an image library, where each pipeline image includes an image of a segment of one of multiple hydrocarbon transport pipelines, the image including one or more patterns of a failure associated with the segment, the failure is associated with one of multiple pipeline failure modes. A convolutional neural network (CNN) is trained using the image library to categorize the multiple pipeline failure modes. An image of a pipeline is received, the pipeline includes a segment with a pipeline failure. The pipeline failure is categorized according to the multiple pipeline failure modes, by processing the image of the pipeline using the CNN. The categorized pipeline failure is provided for generation of a preliminary failure analysis report of the pipeline.