Affiliations: [a] Metallurgical and Materials Department, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil | [b] Physics Institute, State University of Rio de Janeiro, Rio de Janeiro, Brazil | [c] Electrical Engineering Department, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil | [d] Research and Development Center, Petrobras, Rio de Janeiro, Brazil
Correspondence:
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Corresponding author: Ana Carolina Pereira Soares Brandão, Metallurgical and Materials Department, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil. E-mail: anabrandao@coppe.ufrj.br. ORCID: https://orcid.org/0009-0008-3439-0349.
Abstract: HP austenitic stainless steel undergoes microstructural aging due to prolonged exposure to oxidizing and corrosive atmospheres in steam reforming furnaces. The derived aging states are classified by its service temperature and microstructural markers and monitoring it is important to residual life assessment. In this regard it was used a portable Eddy Current inspection system with the aid of machine learning classification tools, characterizing aging states in HP steel in real-time. The classification profile of a 12-meter tube was acquired, validated through Field Metallurgical Replication. The developed Eddy Current inspection system successfully differentiates three regions, revealing a progression of aging states.
Keywords: Reforming furnace, HP steel, eddy currents, machine learning, SVM
