ABSTRACT
Ultrasound nondestructive testing is commonly applied in industry to guarantee structural integrity. HP steel pyrolysis furnaces are used in petrochemical industry for lightweight hydrocarbon production. HP steel chromium content may be reduced in high-temperatures due to carbon diffusion. This characterizes the carburization phenomenon, which modifies magnetic properties, reduces mechanical resistance and may lead to structural rupture. For safe operation it is required to frequently determine carburizing level in pyrolysis furnace pipes. This is traditionally performed manually using magnetic evaluation. This work proposes a novel procedure for carburizing level estimation using ultrasonic evaluation associated to signal processing and machine learning techniques. Experimental data from pulse-echo ultrasonic tests performed in HP steel pipes are used. Discrete Fourier transform was applied for feature extraction and different classification systems (neural networks, k-nearest neighbors and decision trees) are applied and compared in terms of carburizing level identification efficiency.
