Ultrasound-Based Smart Corrosion Monitoring System for Offshore Wind Turbines

The ultrasound technique is a well-known non-destructive and efficient testing method for on-line corrosion monitoring. Wall thickness loss rate is the major parameter that defines the corrosion process in this approach. This paper presents a smart corrosion monitoring system for offshore wind turbines based on the ultrasound pulse-echo technique. The solution is first developed as an ultrasound testbed with the aim of upgrading it into a low-cost and low-power miniaturized system to be deployed inside offshore wind turbines. This paper discusses different important stages of the presented monitoring system as design methodology, the precision of the measurements, and system performance verification. The obtained results during the testing of a variety of samples show meaningful information about the thickness loss due to corrosion. Furthermore, the developed system allows us to measure the Time-of-Flight (ToF) with high precision on steel samples of different thicknesses and on coated steel samples using the offshore standard coating NORSOK 7A.

Macro-strain based deflection and neutral axis position monitoring of reinforced concrete beams during corrosion

This article proposes a new technique of monitoring neutral axis positions and deflection of Reinforced Concrete (RC) beam during corrosion of steel reinforcement using macro-strain measurements of distributed long-gauge sensors. A different group of distributed long-gauge Packaged Carbon Fiber Line (PCFL) sensors with self-compensation and effective packaging system is installed on the compression and tension fibers of the concrete surface and steel reinforcements of RC beam to verify the proposed method experimentally. An accelerated corrosion method utilizing a salt solution and the constant current was used to achieve the required corrosion levels. The estimated deflection measured by the developed method is compared with the results using Linear Variable Displacement Transducer (LVDTs). It has been demonstrated that long-gauge PCFL sensors could provide the same accuracy. The distributed measured strains were utilized to evaluate the deterioration of the structure’s health with the advance of corrosion. Based on corrosion monitoring experimental results, it can be confirmed that using distributed PCFL sensors mounted on steel reinforcements or concrete surface, the locations and progress of the damage with corrosion time can be detected effectively. The maximum error in the estimated deflection from PCFL sensors mounted on the concrete surface compared to the LVDTs before the onset and after 24 h of accelerated corrosion was 0.5% and 2.5%, respectively.

Long-term corrosion monitoring of carbon steels and environmental correlation analysis via the random forest method

In this work, the atmospheric corrosion of carbon steels was monitored at six different sites (and hence, atmospheric conditions) using Fe/Cu-type atmospheric corrosion monitoring technology over a period of 12 months. After analyzing over 3 million data points, the sensor data were interpretable as the instantaneous corrosion rate, and the atmospheric “corrosivity” for each exposure environment showed highly dynamic changes from the C1 to CX level (according to the ISO 9223 standard). A random forest model was developed to predict the corrosion rate and investigate the impacts of ten “corrosive factors” in dynamic atmospheres. The results reveal rust layer, wind speed, rainfall rate, RH, and chloride concentration, played a significant role in the corrosion process.

Field deployable impedance-based corrosion sensor

Electrochemical impedance spectroscopy (EIS) has been used in various applications, such as metal corrosion monitoring. However, many conventional corrosion monitoring setups are bulky and inconvenient for in-situ testing. The purpose of this work is to reduce the size of the whole corrosion monitoring system. We utilized EIS to design a field deployable impedance-based corrosion sensor (FDICS), capable of performing in-situ EIS analysis. Experiments verified the sensor’s accuracy, and the results showed that the sensor performed similarly to a bench-top EIS machine when we tested on circuit models. Furthermore, we used the proposed FDICS to monitor a metal corrosion experiment and performed EIS. The result showed that the proposed FDICS is able to obtain the sample’s impedance spectroscopy, which could help researchers test its corrosion severity on a metallic sample in-situ. Compared to other bulky conventional setups, our device eliminates the design complexity while still showing insights into the corrosion reaction.

A Novel Corrosion Monitoring and Prediction System Utilizing Advanced Artificial Intelligence

Corrosion in pipes is a major challenge for the oil and gas industry as the metal loss of the pipe, as well as solid buildup in the pipe, may lead to an impediment of flow assurance or may lead to hindering well performance. Therefore, managing well integrity by stringent monitoring and predicting corrosion of the well is quintessential for maximizing the productive life of the wells and minimizing the risk of well control issues, which subsequently minimizing cost related to corrosion log allocation and workovers. We present a novel supervised learning method for a corrosion monitoring and prediction system in real time. The system analyzes in real time various parameters of major causes of corrosion such as salt water, hydrogen sulfide, CO2, well age, fluid rate, metal losses, and other parameters. The data are preprocessed with a filter to remove outliers and inconsistencies in the data. The filter cross-correlates the various parameters to determine the input weights for the deep learning classification techniques. The wells are classified in terms of their need for a workover, then by the framework based on the data, utilizing a two-dimensional segmentation approach for the severity as well as risk for each well. The framework was trialed on a probabilistically determined large dataset of a group of wells with an assumed metal loss. The framework was first trained on the training dataset, and then subsequently evaluated on a different test well set. The training results were robust with a strong ability to estimate metal losses and corrosion classification. Segmentation on the test wells outlined strong segmentation capabilities, while facing challenges in the segmentation when the quantified risk for a well is medium. The novel framework presents a data-driven approach to the fast and efficient characterization of wells as potential candidates for corrosion logs and workover. The framework can be easily expanded with new well data for improving classification.