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Convolutional deep neural networks are one of the main machine learning techniques applied to computer vision and object recognition tasks. Currently, they are very popular due to their proven effectiveness in solving image classification tasks and their significant theoretical and practical importance to the advancement of the deep learning field. Examples of successful image classification networks developed are AlexNet, VGG, and GoogLeNet.1,2,3
Corrosion detection in industrial assets and components is an important broad problem in the industries since it allows the temporal tracking of possible issues and the execution of preventive maintenance actions, such as protective coating. However, solving this problem using modern machine learning methods usually demands a careful design of artificial intelligence tools, such as neural networks, high computational resources for training and inference, and a large and adequate dataset. In this work we investigate the application of deep convolutional neural networks to the problem of image semantic segmentation of superficial corrosion and dirt present in mining industrial assets, using a set of images collected in place by corrosion inspectors and manually labeled by a data team. We compare two networks based on the popular U-Net model, in which one of them uses the transferred features from a pre-trained VGG-16 image classification model.
In the mid-1990s, the US Navy’s technical community, led by Naval Sea Systems Command (NAVSEA), recognized existing coatings used to protect the inside of ships’ tanks were failing on average 5-8 years after application. The high cost to blast and recoat over 11,000 tanks every 5-8 years, not counting submarines and aircraft carriers, was prohibitive. To address this issue, the Navy conducted a study to analyze the problem and decided to replace these legacy coatings with high solid epoxy coatings.1
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Integrity management of corrosion under insulation (CUI) has historically and continues to be one of the biggest corrosion related challenges within the oil & gas, maritime, chemical and petrochemical industries.2 Corrosion of piping, associated flanges, pressure vessels and structural components from CUI is a commonly found phenomenon and if left undetected or not stringently managed can result in catastrophic leaks or explosions, equipment failure and periods of prolonged downtime due to repair or replacement. It is estimated around 40% to 60% of an operator’s pipeline maintenance budget is a result of CUI.3
Caustic corrosion is sometimes referred to as “caustic attack or “caustic gouging.” Corrosion of this type may result from internally fouled heat transfer surfaces and the presence of sodium hydroxide in the boiler water; and concentrated solutions of alkali where the normal washing of the tube metal ID is restricted after Departure from Nucleate Boiling (DNB), i.e., when the steam bubble release exceeds the rinsing rate.