Pipeline corrosion defect database construction method for artificial intelligence training

By constructing a correlation database containing corrosion environment parameters, geometric features, and magnetic flux leakage signals, the problem of lack of quantitative mapping in magnetic flux leakage detection technology is solved, and the accurate correspondence between corrosion morphology and signal is achieved, thereby improving the quantitative assessment and prediction capabilities of pipeline corrosion detection.

CN122387933APending Publication Date: 2026-07-14INST OF METAL RESEARCH - CHINESE ACAD OF SCI
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Patent Information

Application Number
CN202610362459.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-24
Publication Date
2026-07-14

AI Technical Summary

Technical Problem

Existing magnetic flux leakage detection technology cannot accurately reflect the true morphology, depth, volume and other key geometric parameters of corrosion pits, lacks quantitative mapping relationships, and is difficult to achieve quantitative assessment of corrosion degree and prediction of remaining life.

Method used

By constructing a multi-scenario corrosion experimental platform, real corrosion defects are prepared. Combined with high-precision three-dimensional measurement and magnetic flux leakage detection, corrosion pit morphology and magnetic flux leakage signal data are collected, and an associated database is established, which includes corrosion environment parameters, geometric features and magnetic flux leakage signal data.

Benefits of technology

It achieves a precise correspondence between corrosion morphology and magnetic flux leakage signal, improves the quantitative assessment capability of pipeline corrosion detection, and supports accurate assessment of corrosion degree and prediction of remaining life.

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Abstract

The invention relates to the technical field of pipeline corrosion detection and data analysis, and provides a pipeline corrosion defect database construction method for artificial intelligence training, which simulates a typical corrosion environment possibly encountered in a pipeline service process by constructing a multi-scene corrosion experiment platform. Real corrosion defects with different morphologies are prepared on the pipeline steel sample through an accelerated corrosion experiment; then acquiring fine morphology data of the corrosion pit by adopting a high-precision three-dimensional measurement technology, and acquiring a magnetic flux leakage signal of a corresponding defect by utilizing standard magnetic flux leakage detection equipment; and finally, performing unified structured processing on the corrosion environment parameters, the corrosion pit geometric characteristic parameters and the magnetic flux leakage detection signal data to form a queriable and extensible associated database. The database can truly reflect the internal relation between the corrosion morphology and the magnetic flux leakage signal, and provides key data support for subsequent pipeline corrosion defect quantitative evaluation, detection model optimization and intelligent inversion algorithm research.
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