This work presents a system proposal for measurement and digital processing of electric potential drop, using a scanning electrode, to get a mapping of surface electric potentials. The processed signal allows to detect the morphology of internal defects such as cracks, corrosion, welding defects and other defects in piping, pressure vessels and others industrial equipments. The proposed technique allowed to infer the electrical image of an internal defect in a stainless steel plate. The technique proposed on this paper offer a real time tool for monitoring and analysis, for the prevention of failure, leakages and avoiding potential incidents in pipes and equipments made of carbon steel, stainless steel, and similar electrical conductive materials, used in installations of petroleum industry, water and sewage treating plants, thermoelectric plants, among others. The proposed solution can be developed to a field equipment to create electrical images based in potential drop. Resumo: Este trabalho apresenta uma proposta de sistema para a medição e processamento digital de sinais de diferença de potencial elétrico, utilizando um eletrodo de varredura, para obter um mapeamento de potenciais elétricos de superfície. Utilizando técnicas de processamento de sinais,é possível detectar a morfologia de defeitos internos, como trincas, corrosão, defeitos em soldas e falhas em tubulações, vasos de pressão e outros equipamentos industriais. A técnica proposta permitiu inferir a imagem elétrica de um defeito interno numa chapa de aço inox. A técnica apresentada oferece uma ferramenta em tempo real para o monitoramento e análise, prevenção de falhas, de vazamentos e de incidentes em tubulações e equipamentos de aço carbono, aço inoxidável e materiais condutores elétricos similares, utilizados em instalações petrolíferas, de tratamento deágua e esgoto, unidades termoelétricas, entre outros. A solução proposta pode resultar num equipamento de campo para criar imagens elétricas baseadas em potential drop.

This work presents a non-intrusive method to obtain information about damages caused by internal corrosion in a stainless-steel plate and classify them according to their severity. The Potential Drop technique provides an electric potential gradient map, which is analyzed by the application of image processing techniques, such as morphological analysis and segmentation. Some corrosion forms can be detected by this method, like cracks and pitting corrosion; the last one is discussed in this paper. Finite Element Modeling simulations were performed to get examples of defective plates (with two classes of damages) The image processing in the simulations acts as a feature extractor that feeds a binary classifier based on Logistic Regression, which accuracy was 99.24%.

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