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.

Petrobras is the biggest FPSO (Floating, Production, Storage and Offloading) operator in the world having around of 25% of the market. The integrity management of such assets will shape the future of the Brazilian oil production in the next years. The implementation of state-of-the-art tools and the development of new solutions via R,D&I projects are important steps on the path to increase the integrity management evolution. Several programs and activities are in course on the three major related areas for the operational assurance of the asset: subsea, topsides and marine (including vessel structure). The inspection and maintenance markets were forced to accelerate the tools and systems development during the last years of Pandemic due to social distancing rules, so the need to remotely control activities and equipment became more important and valuable. The necessity to keep people away of the site has pushed the implementation of wearables, permanent monitoring systems usage, drones, robots and ROVs (Remoted Operated Vehicles), mini-ROVs and AUVs (Autonomous Underwater Vehicle). The operation team is currently investigating new ways to reduce human effort in tasks such as thickness measurement and visual inspection by using recent technologies to improve the inspection quality. This paper aims to outline the integrity management evolution roadmap, which is based on integrated programs on subsea, topsides, and marine inspection and maintenance that, working aligned, will end in a centrally stored collected data from several sources that will be available and displayed in each asset's Digital Twin model.

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%.