Image Analysis-Based Deformation Fault Assessment System and Method for Polymer Pressure Pipelines

By using an image analysis-based polymer pressure pipeline deformation fault assessment system, which combines multi-source images and operating condition data, the system enables segmented and refined management and overall hierarchical determination of polymer pressure pipelines. This solves the problems of low detection efficiency and limited coverage in existing technologies, and improves detection coverage and assessment reliability.

CN121904601BActive Publication Date: 2026-05-26HUNAN INSTITUTE OF ENGINEERING
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
HUNAN INSTITUTE OF ENGINEERING
Filing Date
2026-03-26
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing polymer pressure pipeline health assessment technologies are difficult to achieve segmented, quantitative, and overall hierarchical deformation risk assessment, and have low detection efficiency and limited coverage.

Method used

The image analysis-based polymer pressure pipeline deformation fault assessment system divides the pipeline into sections, combines multi-source image data and operating condition data, establishes a deformation assessment model, outputs deformation assessment factors, and achieves segmented refined management and overall hierarchical judgment.

Benefits of technology

It enables segmented and refined management of the entire pipeline, improves detection coverage and assessment reliability, can quickly locate abnormal areas, reduce unnecessary maintenance costs, and provides dynamic risk analysis and overall deformation fault level determination.

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Abstract

This invention relates to the field of image analysis technology, specifically disclosing an image analysis-based system and method for assessing deformation faults in polymer pressure pipelines. The invention involves uniformly dividing the polymer pressure pipeline under test into several pipeline segments, acquiring multi-source pipeline image data, and simultaneously collecting operational condition data of the pipeline. Based on the multi-source pipeline image data, pipeline deformation feature parameters are extracted, and candidate deformation fault pipeline segments are selected by combining the operational condition data. Using the operational condition data of the candidate deformation fault pipeline segments, a pipeline segment deformation assessment model is established, and pipeline deformation assessment factors are output. Based on the pipeline deformation assessment factors, the deformation state of the candidate deformation fault pipeline segments is analyzed to determine the deformation fault level of the polymer pressure pipeline under test. This enables segmented, refined detection and management, improving detection coverage.
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