Methods For Detection Of Corrosion Research Articles

Methods for Corrosion Detection in Pipes Using Thermography: A Case Study on Synthetic Datasets

This study reviews advanced methods for corrosion detection and characterization in pipes using thermography, with a focus on addressing the limitations posed by small datasets. Thermography captures temperature distributions on the surface of pipes to identify subsurface defects. The challenges of sequential data processing, neural network performance, feature extraction, and dataset size are discussed, with proposed solutions such as advanced algorithms, feature selection techniques, and data augmentation. Given the significant gap in the current literature, there is a need for larger, more diverse datasets to train more robust and accurate machine learning models. A case study combining experimental data with Finite Element Method (FEM) simulations demonstrates that augmenting datasets with synthetic data significantly improves defect detection accuracy. These findings highlight the potential of integrating thermography with machine learning to enhance defect detection, providing insights for future research and practical applications.

Artificial intelligence-based ship hull plate corrosion monitoring using Convolutional Neural Network (CNN): comparison of YOLOv8 and Detectron2 architecture models

ABSTRACT Detecting and monitoring structural damage is crucial to prevent significant damage to ship structures caused by corrosion. This study aims to measure the performance of corrosion detection using Convolutional Neural Networks (CNN) by comparing YOLOv8 and Detectron2 models analyzed at various data augmentation settings. The results show that Detectron2 with the ResNet101 backbone performs better than Detectron2 ResNet50 and YOLOv8 models. The result found that applied augmentation data settings, such as vertical & horizontal flip, rotation, and increasing & decreasing colour saturation, highlight their importance in training robust corrosion detection models. These findings contribute to developing corrosion detection methods, showing that the Detectron2 model with instance segmentation can overcome the complexity and variety of corrosion shapes more effectively than YOLOv8 model. The instance segmentation model has proven to be the more effective CNN model for detecting abstract objects, such as corrosion damage in ship hulls.

Experimental Study on Corrosion of Stainless Steel in Low Temperature Multi effect Seawater Desalination

Starting from the corrosion mechanism, this paper analyzes the characteristics of various types of stainless steel and selects the best performance composite plate composite plate stainless steel. Analyze and select the most suitable corrosion detection method based on specific practical multi working conditions, discuss the interference factors that affect metal corrosion during experimental simulation, and the advantages of newly developed sheet metal. The new development of composite board panels, with the substrate and composite materials applying their respective capabilities for MED, will bring breakthrough progress to the scientific research and engineering application of composite boards.

Quantitative monitoring of localized pitting corrosion in steel bars through a combined use of distributed optical fiber sensors and finite element analyses

Pitting corrosion of steel bars can greatly impair mechanical properties of steel bars, so detecting pitting corrosion timely and accurately is crucial for assessing the health of structures. However, most current corrosion detection methods have some difficulties measuring pitting corrosion level of steel bars effectively. Considering that corrosion pits can cause strain redistribution of steel bar in the tensile state, this study proposes a method to quantitatively determine the pitting corrosion level through monitoring the steel strain distribution. Distributed optical fiber sensors (DOFS) based on Rayleigh scattering and capable of achieving the spatial resolution in millimeter scale were adopted to monitor the strain distributions of notched HRB400E steel bars subjected to tensile stress, and finite element (FE) method was employed to analyze the strain distributions of steel bars with different pit geometries. Experimental results showed that the bare fiber sensors (PI-FS) measured steel strain more accurately than jacketed fiber sensors. From strain distributions monitored by PI-FS, the pit location and pit length can be directly determined. Further, FE results revealed that the strain distribution along the fiber sensor position was significantly affected by the fiber sensor position relative to the pit mouth. Finally, the procedures to determine the pitting corrosion level from DOFS data and FE results were presented by mathematically comparing the similarity of strain curves given by the fiber sensor on a pitting-corroded bar and FE analyses of steel bars with different corrosion levels. In the end, the issues related to the practical applicability of the proposed method were discussed and limitations of this study were addressed. Although with limitations, this study provided a novel and promising way to quantitatively monitor the pitting corrosion level based on the strain distribution of steel bars.

Stress corrosion phenomenon of casing in CO2 medium and magnetic memory detection

Due to being in an acidic CO2 environment and high-pressure underground, the stress corrosion phenomenon on the inner wall of the CCUS wellbore is more pronounced, with the possibility of wall thickness reduction and crack propagation, which affects the integrity and storage reliability of the wellbore. This article conducted indoor experiments on stress corrosion of oil casing, and characterized the stress corrosion through magnetic memory detection technology. Through experiments, the stress corrosion morphology of specimens under different concentrations, stresses, and defects was obtained. The microstructure of the stress corrosion site was observed using SEM electron microscopy, and the stress corrosion situation of the material in CO2 was observed, laying a foundation for magnetic memory detection; A stress corrosion detection method under CO2 environment was proposed, and the magnetic memory detection method was used to measure that the more severe the stress corrosion, the more obvious the magnetic signal mutation; The shape and morphology of different defects vary, but the main factors affecting magnetic memory signals are concentration and loading force. This article verifies the feasibility of magnetic memory in stress corrosion detection through experiments, and characterizes the stress corrosion situation using magnetic memory signals, which is of great significance for non-destructive testing of wellbore stress corrosion in CO2 storage.

A pipeline corrosion detecting method using percussion and residual neural network

Corrosion of pipeline walls can lead to serious safety accidents such as leaks, fires and even explosions. This paper proposes a corrosion detection method using deep learning based on percussion sound for pipelines. The percussion induced acoustic signals are processed by wavelet threshold noise reduction and double threshold endpoint detection to generate the Mel spectrograms, and then an 18-layer residual network (ResNet18) is used to mine the depth information and classify the degree of pipeline corrosion. We conducted experiments to verify the validity of the approach. Seven working conditions are generated by electrochemical corrosion of a pipe specimen, and percussions are applied at five different positions under the same working conditions to collect the impact acoustic signals. The test results show that the method can quickly, efficiently and accurately detect the degree of pipeline corrosion, classify the degree of pipe corrosion without being affected by the striking position Therefore, the model has great potential for application in detecting the internal corrosion of pipelines based on percussion sounds.

Research on a Corrosion Detection Method for Oil Tank Bottoms Based on Acoustic Emission Technology.

This paper presents an acoustic emission (AE) detection method for refined oil storage tanks which is aimed towards specialized places such as oil storage tanks with high explosion-proof requirements, such as cave oil tanks and buried oil tanks. The method utilizes an explosion-proof acoustic emission instrument to detect the floor of a refined oil storage tank. By calculating the time difference between the defective acoustic signal and the speed of acoustic wave transmission, a mathematical model is constructed to analyze the detected signals. An independent channel AE detection system is designed, which can store the collected data in a piece of independent explosion-proof equipment, and can analyze and process the data in a safe area after the detection, solving the problems of a short signal acquisition distance and the weak safety protection applied to traditional AE instruments. A location analysis of the AE sources is conducted on the bottom plate of the tank, evaluating its corrosion condition accurately. The consistency between the evaluation and subsequent open-tank tests confirms that using AE technology effectively captures corrosion signals from oil storage tanks' bottoms. The feasibility of carrying out online inspection under the condition of oil storage in vertical steel oil tanks was verified through a comparison with open inspections, which provided a guide for determining the inspection target and opening order of large-scale oil tanks.

Intelligent Method for Corrosion Detection and Quantification in Aircraft Lap Joints Using Pulsed Eddy Current

Nondestructive evaluation using pulsed eddy current (PEC) has remarkable capabilities for the detection and characterization of hidden corrosion in multilayer structures. Previous works proposed maximum peak value, time-to-peak, and time-to-zero crossing of PEC signals for hidden corrosion detection. In practice, there are two important noise sources: probe liftoff and interlayer gap. These noise sources disable the aforementioned components in defect detection and classification. This paper delivers a new intelligent method for detection and classification of corrosion defects in two-layer structures at the presence of gap and liftoff. Application independent component analysis and principal component analysis to PEC signals for features extraction and Fisher’s linear discriminant method for classification provide an automatic material loss characterization in multilayer structures. Comparing the results with conventional methods based on PEC signals shows the importance of post-processing methods in the detection and quantification of corrosion.

Optimized deep learning for steel bridge bolt corrosion detection and classification

Corrosion of steel bridge bolts poses a major threat to structural integrity. As a key component, bridge bolts are susceptible to corrosion and detachment, necessitating close monitoring during maintenance. However, the vast number of corroded bolts presents challenges. Conventional detection relies on subjective visual inspection without unified assessment criteria. Fast and accurate localization and classification of bolt corrosion levels thus became a research priority for bridge maintenance. This study puts forward a new two-stage corrosion detection method, integrating Deep Convolutional Generative Adversarial Network (DCGAN) and improved You Only Look Once version 5 (YOLOv5) networks, which enables rapid categorization of bridge bolts by different corrosion extents, providing an intelligent solution for bridge maintenance. First, in the image preprocessing stage, a DCGAN with least squares loss generates simulated corroded bolt images through adversarial learning to expand the dataset. Second, three corrosion levels are defined based on visual features and percentage of surface rust, with this classification scheme applied in dataset annotation. Finally, the annotated images are input into an enhanced YOLOv5 model named YOLOv5-SE for corrosion identification. To improve accuracy, Squeeze-and-Excitation blocks are added after each residual block in the YOLOv5 backbone. Comparative experiments prove YOLOv5-SE's superior performance over YOLOv5 and other models like Faster R-CNN and VGG16. A pytorch system enables model deployment for corrosion detection. The integration of DCGAN simulation and attention-optimized YOLOv5-SE enables precise classification and localization of corroded bolts. This study represented a significant step towards intelligent upgrades in steel bridge maintenance.

Research on reinforcement corrosion detection method based on the numerical simulation of ground-penetrating radar

Both the reinforcement depth and the electromagnetic properties of the concrete cover dramatically affect the steel reinforcement reflection wave amplitude in ground-penetrating radar (GPR), resulting in significant errors when evaluating the corrosion status of the reinforcement using the reflection wave amplitude. In response to this issue, this paper proposes a normalized amplitude–two-way travel time–relative permittivity (A-TWTT-εr) method, which, compared to traditional single-shot detection methods, enhances the effectiveness of steel reinforcement corrosion detection through 3D space. The amplitude and TWTT parameters are extracted using the A-scan waveform at the vertex of the steel reinforcement, while εr is determined through hyperbolic feature scatter fitting. Finally, the feasibility of the proposed method was verified using COMSOL numerical simulation data. The results showed that the proposed A-TWTT-εr amplitude-correction method could simultaneously correct for the attenuation effect of differences in the reinforcement depth and concrete electromagnetic properties on the amplitude. The proposed method could enable single-shot corrosion detection by GPR, yet further validation is needed in complex experimental and on-site environments.

Intelligent analysis of corrosion characteristics of steel pipe piles of offshore construction wharfs based on computer vision

The lower part of offshore construction wharfs is mostly a steel structure system composed of steel pipe piles, whose corrosion level directly affects the structural safety performance of steel wharfs in service. The currently common corrosion detection methods can only sample and inspect steel pile after it has been dismantled, making it impractical for in-service monitoring during the operational period of the steel pile. In this paper, a deep learning-based image classification model is first established to recognize the type of corroded area on steel pipe piles. The model achieves a recognition accuracy of 99.14 % in automatically identifying different types of corroded areas, including full immersion zone, tidal range zone, and splash zone. Subsequently, digital image processing technology is utilized to automatically calculate the corroded area of steel pipe piles. The method proposed in this paper can obtain the key information, such as type of corrosion area and area of the steel pipe pile corrosion area, without damaging their structural performance during the service. With this data, the mechanical performance of steel pipe piles can be analyzed, and the structural safety of the in-service steel pipe piles can be determined, thereby ensuring the safety of the construction wharf.

High-Precision Corrosion Detection via SH1 Guided Wave Based on Full Waveform Inversion.

Corrosion detection for industrial settings is crucial for safe and efficient operations. Due to its high imaging resolution, the guided-wave full-waveform inversion tomography technique has significant potential for corrosion detection of plate metals. Limited by the long wavelengths of A0 and S0 mode waves, this method exhibits inadequate detection resolution for the earlier shallow and small corrosion defects. Based on the relatively short wavelength characteristics of the SH1 mode wave, we propose a high-precision corrosion detection method via SH1 guided wave using the full waveform inversion algorithms. By conducting finite element simulations of ultrasonic-guided waves on aluminum plates with varying corrosion defects, a comparison was made to assess the detection precision across A0, S0, and SH1 modes. The comparison results showed that, whether for regular or irregular defects, the SH1 mode wave always exhibited higher imaging accuracy than the A0 and S0 mode waves for shallow and small-sized defects. The corresponding experiments were conducted on an aluminum plate with simple or complex defects. The results of the experiments reconfirmed that the full waveform inversion method using SH1 guided wave can effectively reconstruct the shape and size of small and shallow corrosion defects within aluminum plates.

A dual attention network for automatic metallic corrosion detection in natural environment

Industrial and residential facilities worldwide are exposed to significant costs and hazards due to metallic corrosion in urban systems. It is necessary to develop a method to automatically detect metallic corrosion precisely. However, existing metallic corrosion detection methods cannot detect either from the realistic natural images or from all kinds of metallic erosion with a high accuracy rate. In this work, a Dual Attention Metallic Corrosion Network (DAMCN) is proposed to automatically detect the metallic corrosion regions with various sizes and shapes from digital images, which are collected in natural environment by unmanned aerial vehicles. By embedding the dual attention modules (i.e., the CBAM blocks) into different locations of the backbone network (i.e. Mask-RCNN), important metallic corrosion features are enhanced either in the feature encoding (i.e. DAMCN-V1) or in the multi-scale feature extraction (i.e. DAMCN-V2). Extensive experiments on three benchmark datasets validate the effectiveness of the proposed method on metallic corrosion detection with varying region size and shape. Comparison results among our DAMCN-V1 and DAMCN-V2 to other existing related methods also demonstrate the superiority of our DAMCN-V1, which achieves the best result while its counter-part (DAMCN-V2) comes to the second.

Simulation and Experimentation of a Grounding Network Detection Scheme Based on a Low-Frequency Electromagnetic Method.

The grounding network is a significant component of substations, and the corrosion of its ground resistance is predominantly detected using the electromagnetic method. However, the application of electromagnetic methods for detecting corrosion within earthing networks has received relatively limited attention in research. Currently, the prevailing method utilizes electromagnetic techniques to identify the breakage points within the given earthing network. In this study, we propose a corrosion detection method for grounding networks based on the low-frequency electromagnetic method, which measures the resistance value between individual nodes of the network. Specifically, an excitation source signal of a predetermined frequency was transmitted to the measurement segment of the grounding network, which facilitated the direct measurement of the strength of the induced magnetic field above the center of the measuring conductor. The recorded electromagnetic data were subsequently uploaded to the host computer for data processing, and the computer interface was constructed based on a LABVIEW design. By leveraging the relationship between the induced electric potential, current strength, excitation source strength, and additional voltage detection devices, the resistance of the conductor under examination could be determined. Furthermore, the proposed method was tested under suitable conditions, and it demonstrated favorable results. Thus, the proposed method can serve as a foundation for developing electromagnetic testing instruments tailored to the investigated grounding network.

Artificial intelligence-based corrosion sensing and prediction for aircraft applications (AICorrSens)

Corrosion causes enormous damage to mechanical structures in many industrial sectors, and the aviation industry is no exception. To extend the lifetime of airframes without compromising safety, it is very important to have a clear picture of the state of corrosion (SoC) of the aircraft. Thus, it is essential to develop methodologies suitable for real-time monitoring of the SoC and subsequent reliable notification when a structure has been compromised by corrosion. Published results so far suggest that the ultrasonic (e.g. acoustic emission, guided waves) as well as electrochemical sensors (e.g. electrochemical noise, impedance spectroscopy) are suitable for monitoring aircraft-relevant corrosion but lack the technological readiness to be applied in commercial aircraft yet. A huge issue in achieving reliable monitoring systems is the correlation between corrosive phenomena and (typically) noisy sensor data. The AICorrSens project addresses these issues by developing a multisensor setup for monitoring the SoC based on ultrasonic, electrochemical, and environmental sensors coupled with AI algorithms. Training data shall be generated by performing accelerated corrosion tests with coupons and demonstrator parts equipped with sensors. Using AI for the subsequent data analysis, one can overcome operational noise, and thus, allow today’s corrosion detection methods onboard real- time evaluation of the SoC in terms of detection, localization, quantification, and typification. The ambition of the project is to transform the created continuous stream of data into classifications of the SoC that are intuitively understandable through a human-machine interface, including a qualified corrosion prediction by the AI models generated from test campaigns. The project results shall lead to increased aircraft safety and reliability and deliver a clear economic benefit for aircraft operators as it allows a switch from regular inspection intervals to condition-based maintenance. Funded by: Austrian Research Promotion Agency Program: Take Off, Call 2019 Consortium: CEST Competence Centre for Electrochemical Surface Technology (CEST), Johannes Kepler University Linz – Institute of Structural Light-weight Design (IKL), Danube University Krems – Department for Integrated Sensor Systems (DISS), Senzoro GmbH (SENZ). Project duration: 10/2020 – 09/2023. EWGAE 35, Ljubljana, Slovenia, 13th – 16th Sep. www.ewg

Spectroscopic and Morphologic Investigation of Bronze Disease: Performance Evaluation of Portable Devices

Copper alloy artworks are particularly subjected to chloride attack, which may trigger bronze disease. Therefore, early identification of the phenomenon is crucial in order to stabilize the reactive copper chloride (CuCl) and remove the harmful corrosion products (atacamite and polymorphs). Confocal Raman Microspectroscopy (CRM) has proven to be effective for the detection of small amounts of atacamite, ascribable to the initial phases of corrosion. The handling of bronze artworks is often difficult or even impossible given their large size and weight, and sampling is not always allowed, making the use of portable instruments mandatory for on-site diagnostics. This paper proposes a method for the early detection of corrosion using non-invasive approaches. In this work, we present the results obtained from a set of artificially aged bronze samples with a suite of either laboratory (bench-top) or field (portable/transportable) instruments with the aim of highlighting their characteristics and performances in the diagnosis of bronze disease. Raman spectroscopy, Fiber Optics Reflectance Spectroscopy (FORS), Optical Coherence Tomography (OCT), and Scanning Electron Microscopy (SEM) were applied for chemical and morphological characterization of the samples.

Corrosion Detection Method of Transmission Line Components in Mining Area Based on Multiscale Enhanced Fusion

Component corrosion is one of the potential safety hazards in transmission lines in mining areas. In order to solve the problem of poor detection accuracy caused by the large proportion of small targets and complex background in the current distant view corrosion inspection task by UAV, we propose a PWR-YOLOV5 detection method for corrosion components based on the YOLOV5 algorithm. Firstly, a new feature fusion network, WA-PANet, is reconstructed on the basis of the path aggregation network (PANet) to make full use of the features at different stages and advance the detection accuracy of small targets in distant view by deepening the process of feature fusion and introducing the skip layer connections and adaptive feature fusion factors. Secondly, the pyramid split attention (PSA) module is introduced into the deep layers of the network to highlight the feature expression of corrosion targets and enhance the ability to detect pixel-level objects. Then, we construct a receptive feature enhancement network (RFENet), which can heighten the feature fusion effect of the WA-PANet and alleviate the problem of the feature expression ability weakening due to the fusion of different receptive field features. Finally, the EIoU Loss is adopted to optimize the loss function and improve the positioning accuracy of the bounding box. The experimental results show that the mAP of the PWR-YOLOV5 algorithm can reach up to 95.37%, which is 5.22% higher than YOLOV5, and the detection speed is 64.9FPS. Compared with the algorithms such as YOLOV4, Faster R-CNN, and YOLOX, the improved algorithm has better overall detection performance for the corrosion components of transmission lines in the mining area.

A Basic Study on GMR Sensor-Based Eddy Current Nondestructive Testing Method for Corrosion Detection in Concrete Structures

A Basic Study on GMR Sensor-Based Eddy Current Nondestructive Testing Method for Corrosion Detection in Concrete Structures

Quantitative corrosion detection of reinforced concrete based on self-magnetic flux leakage and rust spot area

The internal rebar corrosion of reinforced concrete (RC) structures harm the bearing capacity and durability of structures. Existing methods can measure rebar corrosion but are unsuitable for RC structures. Given this, the self-magnetic flux leakage (SMFL) field of V-shaped rebar corrosion damage was analyzed according to the magnetic dipole model, and the parameter K was proposed to characterize the corrosion degree. Using the naive Bayes algorithm, the SMFL method and the rust spot area analysis method were correlated to propose a rebar corrosion detection method. A corrosion detection experiment was conducted on RC specimens. The results showed that the parameter K was linearly correlated with the maximum cross-sectional rust loss rate η. Using the parameter K to evaluate the rust degree, the accuracy of rebar rust classification was 70%. After introducing the rust spot area ratio S as a supplementary parameter, the accuracy of rebar rust classification increased by 12.5% to 82.5%. This indicates that the proposed method could quantitatively detect the corrosion of the rebars within the concrete.

Reliability of Non - Destructive Technologies in Corrosion Detection of Reinforced Concrete Structures

Abstract In this paper, the reliability of two nondestructive corrosion detection methods, Half-Cell Potential (HCP) and Electrical Resistivity (ER), was evaluated, considering the Weight Loss (WL) as the reference method. The specimens prepared for this work were placed in Al-Nasiriya Power Station where subjected to three different exposure conditions. They were high relative humidity, high temperature T and laboratory setting R. The concrete mixes included four chloride contents, 0 %, 1.5 %, 3 % and 4 % by weight of cement to generate multiple corrosion degrees. The results showed that exposure to high temperatures has slightly increased the corrosion activity of the T specimens. While the exposure to high relative humidity has clearly increased the corrosion probability of the RH specimens, comparing with the reference R specimens. However, for such environmental conditions, it was found that the reliabilities of the HCP and the ER techniques still questionable and need further investigation.