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Carburization level identification in industrial HP pipes using ultrasonic evaluation and machine learning
Lucas F M Rodrigues1, Fábio C Cruz2, Moisés A Oliveira3
1Electrical Engineering Department, Federal University of Paraná, Curitiba, Brazil.
Ultrasonics
|December 12, 2018
Summary
This study introduces a new ultrasonic testing method to detect carburization in high-pressure steel pipes, improving safety in petrochemical operations. The technique uses signal processing and machine learning for accurate defect identification.
Area of Science:
- Materials Science
- Nondestructive Testing
- Petrochemical Engineering
Background:
- High-pressure (HP) steel is crucial in petrochemical furnaces for hydrocarbon production.
- Carburization, a process reducing chromium content in HP steel at high temperatures, degrades magnetic and mechanical properties, risking structural failure.
- Accurate monitoring of carburization levels in furnace pipes is essential for safe industrial operation.
Purpose of the Study:
- To develop and evaluate a novel ultrasonic testing (UT) procedure for estimating carburization levels in HP steel pipes.
- To replace traditional manual magnetic evaluation methods with a more efficient and accurate automated technique.
- To assess the effectiveness of signal processing and machine learning algorithms in identifying carburization.
Main Methods:
- Pulse-echo ultrasonic tests were conducted on HP steel pipes with varying carburization levels.
- Discrete Fourier transform was employed for extracting relevant features from ultrasonic signals.
- Machine learning classifiers, including neural networks, k-nearest neighbors, and decision trees, were implemented and compared.
Main Results:
- The proposed ultrasonic evaluation method, combined with signal processing and machine learning, demonstrated efficiency in identifying carburization levels.
- Feature extraction using Discrete Fourier transform proved effective in characterizing the ultrasonic signals influenced by carburization.
- Comparative analysis showed varying identification efficiencies among the tested classification systems.
Conclusions:
- Ultrasonic testing integrated with signal processing and machine learning offers a promising alternative for monitoring carburization in HP steel pipes.
- The developed methodology can enhance the safety and reliability of petrochemical furnace operations.
- Further research can optimize classifier performance and explore broader applications of this technique.
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