Methods For Detection Of Corrosion Research Articles

Experimental approach for early corrosion detection in pipelines using contact thermometry

ABSTRACT The goal of this work is to propose a novel non destructive pipeline testing method for corrosion detection using contact thermometry. This method is based on surface temperature measurements of pipe using thermistor probes having an accuracy of ±0.01 K. The thermistor probes could identify the subtle change in surface temperature due to rust when there was uniform heat flux inside the pipe. The coordinates of thermistor probes placed in predetermined locations were used to determine the location of the rust. A methodology was proposed for computer aided detection of rust from this surface temperature measurements. To this end, we established a thermal model using heat transfer equation in a hollow cylinder for the simulation of temperature distribution of pipe. The heat transfer equation was solved by finite element method in polar coordinates. The thermal conductivity of the suspicious region was estimated using the Levenberg Marquardt method for identifying the rust location. Thermal images of the 2D circular cross section of the pipe were generated for further analysis. The concept was validated with simulation results as well as with laboratory experiments. The results show that, regardless of pipe thickness, this method can detect rust in its early stages.

Acoustic emission corrosion feature extraction and severity prediction using hybrid wavelet packet transform and linear support vector classifier

Corrosion in carbon-steel pipelines leads to failure, which is a major cause of breakdown maintenance in the oil and gas industries. The acoustic emission (AE) signal is a reliable method for corrosion detection and classification in the modern Structural Health Monitoring (SHM) system. The efficiency of this system in detection and classification mainly depends on the suitable AE features. Therefore, many feature extraction and classification methods have been developed for corrosion detection and severity assessment. However, the extraction of appropriate AE features and classification of various levels of corrosion utilizing these extracted features are still challenging issues. To overcome these issues, this article proposes a hybrid machine learning approach that combines Wavelet Packet Transform (WPT) integrated with Fast Fourier Transform (FFT) for multiresolution feature extraction and Linear Support Vector Classifier (L-SVC) for predicting corrosion severity levels. A Laboratory-based Linear Polarization Resistance (LPR) test was performed on carbon-steel samples for AE data acquisition over a different time span. AE signals were collected at a high sampling rate with a sound well AE sensor using AEWin software. Simulation results show a linear relationship between the proposed approach-based extracted AE features and the corrosion process. For multi-class problems, three corrosion severity stages have been made based on the corrosion rate over time and AE activity. The ANOVA test results indicate the significance within and between the feature-groups where F-values (F-value>1) rejects the null hypothesis and P-values (P-value<0.05) are less than the significance level. The utilized L-SVC classifier achieves higher prediction accuracy of 99.0% than the accuracy of other benchmarked classifiers. Findings of our proposed machine learning approach confirm that it can be effectively utilized for corrosion detection and severity assessment in SHM applications.

A channel attention based deep neural network for automatic metallic corrosion detection

Metallic corrosion in civil infrastructure systems has incurred astronomical costs and considerable risks to industrial communities worldwide. Most existing research focuses on manually extracted features or employing complex convolutional neural networks for powerful deep feature learning on images of metal sheets. But few of them pay attention to learning discriminative deep features for metallic corrosion detection. In this paper, we propose a Channel Attention based Metallic Corrosion Detection method (CAMCD), by which the corroded regions with multiple distinct levels can be automatically detected patch-wisely. Correspondingly, to learn the patch-wise features and discriminate them among various corrosion levels, a CAMCD network is built by embedding SE blocks into the deep residual network; thus, the important features of various corroded regions are highlighted by weighting with the learned weights of channel attentions. Experimental results on our collected metallic corrosion dataset validate the superiority of our proposed CAMCD method over other existing approaches on corroded region detection. And the visualizations of the feature maps weighted by the channel attention further confirm the effectiveness of our CAMCD network on discriminative feature learning.

Qualitative Comparison of 2D and 3D Atmospheric Corrosion Detection Methods.

In this article, we report the use of a Confocal Laser Scanning Microscope (CLSM) to apply a qualitative assessment of atmospheric corrosion on steel samples. From the CLSM, we obtain high-resolution images, together with a 3D heightmap. The performance of four different segmentation algorithms that use the high-resolution images as input is qualitatively assessed and discussed. A novel 3D segmentation algorithm based on the shape index is presented and compared to the 2D segmentation algorithms. From this analysis, we conclude that there is a significant difference in performance between the 2D segmentation algorithms and that the 3D method can be an added value to the detection of corrosion.

Corrosion detection and severity level prediction using acoustic emission and machine learning based approach

Failure caused by corrosion in industries are the major cause of breakdown maintenance. Acoustic emission during the accelerated corrosion testing is a reliable method for corrosion detection, however, classification of these acoustic emission signals by machine learning techniques is still in its infancy. Proposed approach uses a hybrid technique that combines the detection of corrosion through acoustic emission signals from accelerated corrosion testing with machine learning techniques to accurately predict the corrosion severity levels. Laboratory based experimentation setup was established for accelerated corrosion testing of mild steel samples for different time spans and mass loss of samples were recorded. Acoustic emission signals were acquired at high frequency sampling rate with Sound Well AE sensor, NI Elvis kit and NI Labview software. AE mean, AE RMS, AE energy, and kurtosis were selected as distinct features as they represent a linear relationship with the corrosion process. For multi-class problem, five Corrosion severity levels have been made based on mass loss occurred during accelerated corrosion testing for which Naive Bayes, BP-NN and RBF-NN showed accuracy of 90.4%, 94.57%, and 100% respectively.

A novel corrosion detection method using wavelet transformed mode shapes of a functionally graded rotor-bearing system

Development of online fault detection based on vibration characteristics of a rotating system has been the subject of interest for the last few decades. A novel corrosion detection method is suggested in the present work, based on the wavelet transformed modal analysis of a functionally graded (FG) rotor system. Material properties of an FG shaft are position and temperature dependant and graded in a radial direction following the power-law and non-linear temperature distribution (NLTD) respectively. An FG rotor consisting of a mixture of Zirconium Dioxide (ZrO2) and Stainless Steel (SS) is considered, where the volume fraction of metal decreases toward the outer radius and ceramic volume fraction increases. The finite element (FE) formulations have been developed using Timoshenko beam theory (TBT) for modal analysis of a corroded FG rotor bearing system. A spatial variation of a wavelet transform is used to transform the computed FG rotor mode shapes into the wavelet domain to identify and locate the local corrosion defect. A sensitivity analysis and a parametric study has been carried out to further verify the validity of the suggested novel method.

Monitoring of corrosion effects in pipes with multi-mode acoustic signals

Current commercial long-range guided wave inspection and monitoring technologies for pipes usually require establishment of certain specific acoustic modes for wave propagation and use a large number of sensors in a certain geometric arrangement (for example, uniformly-spaced arrays of sensors on a collar, placed around a pipe). This paper, in contrast, presents a long-range corrosion detection method in pipes based on multi-mode guided acoustic waves, generated and received using simple, single-element sensors. In this technique, available guided wave modes in any interrogated zone of the structure are automatically down-selected by the medium, and changes in energy distribution over the selected modes are analyzed through signal processing to monitor material loss or addition. To investigate the feasibility of the proposed method, we have performed both experimental and numerical simulation studies on 50.8-mm (2-in)-diameter schedule-40 carbon steel pipes. In these studies, a linear chirp signal is injected in compression mode, perpendicular to the surface of the pipe, generating multi-mode, multi-directional guided waves that travel within the pipe wall. Corrosion effects in the pipes are simulated numerically and experimentally. To reduce errors in the measurement technique due to changes in the local or global temperature of the measured media, a universal temperature compensation algorithm is introduced. The experimental results show good agreement with simulation results, indicating that the proposed multi-mode wave generation and detection in a pipe could be used for long-range detection and monitoring of corrosion effects, such as pipe wall losses or solid material deposits.

Inspection and Assessment of Corrosion in Pretensioned Concrete Bridge Girders Exposed to Coastal Climate

The most common methods for detecting chloride-induced corrosion in concrete bridges are half-cell potential (HCP) mapping, electrical resistivity (ER) measurements, and chloride concentration testing, combined with visual inspection and cover measurements. However, studies on corrosion detection in pretensioned structures are rare. To investigate the applicability and accuracy of the above methods for corrosion detection in pretensioned bridge girders, we measured pretensioned I-shaped girders exposed to the Norwegian coastal climate for 33 years. We found that, even combined, the above methods can only reliably identify general areas with various probabilities of corrosion. Despite severe concrete cracking and high chloride content, only small corrosion spots were found in strands. Because HCP cannot distinguish corrosion probability in the closely spaced strands from other electrically connected bars, the actual condition of individual strands can be found only when concrete cover is locally removed. Wet concrete with high chloride content and accordingly low HCP and low ER was found only in or near the girder support zones, which can therefore be considered the areas most susceptible to chloride-induced corrosion. We conclude by proposing a procedure for the inspection and assessment of pretensioned girders in a marine environment.

Novel Method for Measurement of Rebar State of Cement Tower

Reinforced concrete (RC) structures are applied widely in low-voltage transmission towers. However, the structural damage caused by the corrosion or fracture of rebar in cement towers seriously affects the safety of transmission lines. Thus, the accurate detection of rebar can effectively maintain cement towers. There are many methods for detecting rebar corrosion, but most of them cannot accurately locate the corrosion, and they do not consider the factors such as the depth of the rebar and the different types of rebar. Therefore, this article proposes a rebar corrosion detection method based on the change of peak slope of magnetic flux density. First, this article analyzes the common pitting corrosion in engineering, then theoretically analyzes the principle of magnetic induction detection of rebar. Then the magnetic flux density peak and the measured position on the 2-D coordinate are plotted, and the rebar corrosion is detected according to the slope. The peak function of flux density without corrosion is fit by the least squares method and the corrosion criterion is defined. Finally, the proposed method is verified through simulation and experiment.

Effect of medium entropy alloy powder-core wire on friction wear and corrosion resistance of arc cladding additive layer

AbstrastLow carbon steel was suitable for industrial fields due to its low price, wide application range and excellent comprehensive performance. However, there were still shortcomings such as poor wear resistance and corrosion resistance in special occasions. In hence, this paper carried out a study on the strengthening process of low-carbon steel parts surface deposition. The TIG welding arc welding method was used to study the strengthening performance of the arc-enhanced medium entropy alloy additive layer. To analyze the elements and microstructure of the medium entropy alloy additive layer by x-ray diffractometer(XRD), scanning electron microscope (SEM), energy dispersive spectrometer (EDS) and other modern analysis methods. The micro-hardness, friction and wear and electrochemical corrosion detection methods were used to study the friction and wear and corrosion resistance of the medium entropy alloy additive layer. The results show that the microstructure of the medium entropy alloy additive layer is a typical lamellar pearlite, which is wrapped with face-centered cubic solid solution (FCC) and unevenly distributed in the additive layer. The microhardness of the medium entropy alloy additive layer is significantly higher than that of the substrate. As the friction and wear load increases, the friction coefficient of the medium entropy alloy additive layer gradually decreases. The wear rate of the additive layer is much lower than that of the substrate and the wear resistance is doubled compared to the substrate. The form of wear is mainly abrasive wear and fatigue spalling wear. The medium entropy alloy additive layer has excellent corrosion resistance. Its corrosion rate is about one tenth of the substrate. The arc cladding medium entropy alloy powder core wire can meet the surface strengthening requirements of low-carbon steel parts and provide engineering basis for the low-carbon steel large-scale, multi-domain, high-level application.

Non-destructive Test Methods for Corrosion Detection in Reinforced Concrete Structures

Abstract Several inspection methods can be used to assess the corrosion state of steel reinforcement in concrete. Especially for periodical field surveys and monitoring, non-destructive testing (NDT) methods are to be preferred as they do not cause any or very limited damage to the existing concrete. In this paper, the corrosion state of three reinforced concrete beams exposed to marine environment for 25 years was evaluated by measuring three parameters; electrochemical potential, concrete resistivity and corrosion rate. The measurements were performed with commercial devices. It was found that all devices are applicable for field inspections. Among the methods selected for the study, the electrochemical potential measured in a fine grid and analysed statistically offered the best possibility of evaluating the corrosion state; preferably in combination with selected excavations for determination of the level of corrosion.

Wall Thickness Measurement of Pipes Using Digital Radiography and Three Wavelet Methods

Digital X-ray radiography is a well established method for non-destructive detection of corrosion and measurement of wall thickness of pipes. Due mainly to the unavoidable detection of scattered X-rays as well as the ever-present system electronic noise, the quality of the acquired radiographs is often low; the quality can however be enhanced by applying various image processing methods. In this research, Wavelet Transform (WT), Gabor Filters (GF) and Shearlet Transform (ST) have been utilized in a background removing algorithm to achieve the required radiographic image quality improvement. The algorithm first decomposes the image into different levels using the WT, GF or the ST method, and the automatic thresholding level, which is based on Rayleigh distribution, is then applied. The output images (with the background removed) were then reconstructed by subtracting the modified component from the original radiograph. The results of the study showed that the reconstructed images yield significantly greater contrast than the original radiograph, the wall thickness measurements have higher precision and defect detection has greater specificity for defect in all regions. Also, the outcomes show that the algorithm has high analytical efficiency and is capable of accurately measuring pipe wall thicknesses and corrosion regions.

A hidden corrosion detection method based on robust multimodal Lamb waves

A Lamb wave is a kind of elastic wave propagating in a plate-like structure with stress distributing through the whole thickness of the plate. A Lamb wave is multimodal, and each mode has unique through-plate-thickness stress profiles, called wave structure. The existence of a specific structure flaw at a certain position in the thickness direction will weaken the ability of the plate to support a specific component of stress, which means each Lamb wave mode would interact with structure flaws in a different way, or that each Lamb wave mode has a unique sensitivity to flaws. Conventionally, a sensitive single mode has been selected for defect detection. However, due to the inconsistency in actuators or sensors, the robustness of detection is limited. In this paper, the difference in the sensitivity of each mode to flaws, rather than a single mode, was used for defect detection. Based on this idea, numerical and experimental studies of the interaction between hidden corrosion and multiple modes were first conducted. Then, a new index, the relative amplitude coefficient, which evaluates the difference in sensitivity between lowest-order modes and higher-order modes, was proposed. Next, by using a double crosshole array and combining it with a probabilistic reconstruction algorithm, a multimodal Lamb wave corrosion detection method was developed. The results show greater robustness in corrosion location and better accuracy, compared with the location results of conventional single mode defect detection. Furthermore, by applying the double sensor strategy, the effective operating frequency range is extended and the location accuracy enhanced.

A Corrosion Detection Method for Steel Strands Based on LC Electromagnetic Resonance

At present, prestress detection is a worldwide problem. Corrosion has an important impact on the accuracy of the stress detection results of prestressed tendons. Once the steel strands in service is corroded, stress corrosion will occur, which seriously affects the safe operation of the prestressed structure. Based on the LC electromagnetic resonance circuit to detect the prestressing force of steel strands, this research has been successfully applied to the detection of the corrosion rate of steel strands, which lays the foundation for the nondestructive detection of the healthy state of steel strands in the existing structure. In this study, the steel strands were regarded as an external inductor embedded in the LC resonance circuit. After the steel strands was corroded, its electromagnetic characteristics would change, and this change was manifested by the resonance frequency. In this paper, the regular change of the resonance frequency caused by the change of the inductance of the steel wire after corrosion was explained theoretically, and a theoretical model of the relationship between the electromagnetic resonance frequency and the corrosion rate was established. In this study, it was proved through experiments that the LC electromagnetic resonance method can accurately detect the overall corrosion degree of bare steel stranded wires, and a fitting formula for corrosion detection of steel stranded wires was established. Through the analysis and exploration of the test data and test phenomena, it has been found that the action mechanism of the stranded wire decreases with the increase of the corrosion rate and the inductance thereof, which leads to the increase of the resonance frequency. The discovery of these laws will provide an improved basis for the prestress detection of in‐service steel strands by LC resonant circuits and make the method to be further applied to practical engineering.

Structural monitoring system for proactive detection of corrosion and coating failure

Performance and availability of high priority structures, systems and products can be greatly affected by corrosion damage. The application of protective coatings, frequent inspections and scheduled based maintenance activities result in huge direct and indirect financial loss to organisations. The expeditious detection of coating failure and corrosion damage can result in precise and cost-effective condition-based maintenance. Coating failure and corrosion phenomena are driven by complex multi-disciplinary parameters according to extensive research findings in the literature. State-of-the-art prognostic models proposed in recent years incorporate complex multi-disciplinary parameters, therefore a real-time prognostic monitoring system must acquire these complex parameters to allow accurate prediction. The work reported here covers the development of a real-time monitoring system by using micro-sensors and includes the validation of the system through accelerated corrosion and coating failure testing. The system contains a remote terminal unit that includes a linear polarisation method for corrosion detection under the coating and a micro-strain gauge method for monitoring stress behaviour over the coating. The software at a base station includes a graphical user interface and database to store parameters for further processing and failure prediction. Real-time monitoring system can be applied to remote, stationary and mobile assets to monitor mechanical and chemical changes within coating-substrate systems.

Corrosion Detection for Large Steel Structure base on UAV Integrated with Image Processing System

Large cranes and other outdoor large steel structures need to be checked regularly to ensure their safety, and corrosion detection is one of the most important inspection item. Traditional manual inspection has many disadvantages including high labour intensity, low efficiency and low detection accuracy. In this paper, a novel corrosion detection method based on UAV intelligent image recognition is proposed. The six-rotor UAV can collect the photos of the steel structure device safely and reliably. With the intelligent corrosion recognition algorithm proposed in this paper, infection such as background noise can be eliminated, and the corrosion area in the image can be identified automatically with high precision. A test device has been built and the results show that the device and method can identify the corrosion area effectively.

Corrosion Mechanism and Corrosion State Detection Method of the Grounding Grid

Corrosion of grounding grids can affect the safe and reliable operation of power systems. In view of the corrosion problem of the grounding grid, this paper introduces the corrosion mechanism, which is chemical corrosion and electrochemical corrosion respectively. Then, three common grounding grid corrosion detection methods, such as electric network analysis method, electromagnetic field detection method and electrochemical method, are introduced. On this basis, the advantages and disadvantages of three methods are compared. Further prospects for the corrosion detection technology of the grounding grid are pointed out.

Benchmarking parameters for remote electrochemical corrosion detection and monitoring of offshore wind turbine structures

AbstractThe remote location and position of offshore wind turbine structures severely limits the application of in situ corrosion detection methods such as ultrasonic, acoustic emission, and X‐ray. A real‐time remote sensing (RTRS) technology can be implemented to provide autonomous detection and monitoring, providing exhaustive and detailed information on the corrosion process. Utilizing the concept of Internet of Things (IoT) through the integration with satellite and terrestrial communication network, iWindCr, a technology development project funded by the Innovate UK, aims to design a wireless sensor network (WSN) of smart miniaturized sensors for corrosion detection and monitoring of the offshore wind turbine structures. This paper discusses the rationale and challenges around the iWindCr WSN design, particularly in the development of a miniaturized system and in relation to the provision of power and power consumption. The later has led to the selection and the integration of the electrochemical analysis techniques, namely open circuit potential (OCP) and zero resistance ammeter (ZRA) on the sensor interface system. The verification of these techniques for the corrosion detection sensor has resulted in a database consisting of the corrosion parameter outputs or threshold values of metals specific to offshore wind turbine structures, in this case tower, foundation, and nacelle (gearbox). The database provides end‐users with the benchmark that can be used to detect physical changes during the course of corrosion or passive film damage. These parameters are incorporated in the user interface data analytic software, enabling the quantification of corrosion or film damage.

Experimental study of the influence of crack width due to corrosion behaviour using different measurement methods and different exposures

Corrosion of reinforcing steel has a great influence in reducing the lifetime of concrete structures; Carbonation of the concrete pore solution causes surface corrosion on the steel and diffusion of chloride ions through the capillary system of the concrete cover causes pitting corrosion on the steel surface. Corrosion of metals is highly dependent on the environmental conditions. Exposure to chloride ions can be critical to the service life of reinforced concrete structures. The durability of reinforced concrete structures exposed to deicing salt or marine environments can be affected by impact of chloride ions. Detection methods for the rate of corrosion of non-destructive and destructive procedures were analysed. The potential mapping applied on the concrete surface was discussed as a standard method for corrosion detection and will be explained in detail including the application boundaries of the method. It is assumed that the corrosion behaviour of reinforcing steel depends on crack widths. To analyse that, 8 coated and 8 uncoated test samples with different concrete strength classes were used. The concrete objects were exposed to a 3% sodium chloride solution. The corrosion behaviour of reinforcing steel is analysed by using potential mapping with different reference electrodes (Ag/AgCl and Cu/CuSO4). The results show a significant correlation between crack size and protection system on the surface. The maximum crack width with a low indication of corrosion was found to be 0.1 mm.

Experimental study of the influence of crack width due to corrosion behaviour using different measurement methods and different exposures

Corrosion of reinforcing steel has a great influence in reducing the lifetime of concrete structures; Carbonation of the concrete pore solution causes surface corrosion on the steel and diffusion of chloride ions through the capillary system of the concrete cover causes pitting corrosion on the steel surface. Corrosion of metals is highly dependent on the environmental conditions. Exposure to chloride ions can be critical to the service life of reinforced concrete structures. The durability of reinforced concrete structures exposed to deicing salt or marine environments can be affected by impact of chloride ions. Detection methods for the rate of corrosion of non-destructive and destructive procedures were analysed. The potential mapping applied on the concrete surface was discussed as a standard method for corrosion detection and will be explained in detail including the application boundaries of the method. It is assumed that the corrosion behaviour of reinforcing steel depends on crack widths. To analyse that, 8 coated and 8 uncoated test samples with different concrete strength classes were used. The concrete objects were exposed to a 3% sodium chloride solution. The corrosion behaviour of reinforcing steel is analysed by using potential mapping with different reference electrodes (Ag/AgCl and Cu/CuSO4). The results show a significant correlation between crack size and protection system on the surface. The maximum crack width with a low indication of corrosion was found to be 0.1 mm.