A Methodology Based on 1D-CNN and Bootstrap Method to Estimate the Remaining Useful Life of Industrial Assets Suffering from Generalized Corrosion

Abstract

The main goal of this work is to propose an efficient and accurate modern methodology to estimate the useful life of industrial assets used in oil and gas industry suffering from generalized corrosion. It merges the convolutional neural networks, extreme value theory, and bootstrap methods to handle the available corrosion data obtained through nondestructive inspection techniques for structural integrity assessments. It is due to the high cost of inspection techniques actually used in many industries to generate a reliable large amount of data to be analyzed by traditional statistical tools and technical factors, such as the inaccessibility of certain zones of the assets. First, the most appropriate extreme value distribution is determined to best fit the available inspection data, aiming to generate sufficient information for the training and testing processes of a one-dimensional convolutional neural network model to improve the accuracy of the useful life estimation. To demonstrate the main features and capabilities of the methodology, the dataset of AISI 1018 steel tubes of a heat exchanger used in a Brazilian refinery subjected to a general corrosion-type extreme process is retained herein. The results demonstrate that it is an interesting tool for inspection process to assist engineers and/or users in predictive maintenance phases to access the structural integrity of industrial assets subjected to extreme events such as general corrosion.