Prospect of Using Machine Learning-Based Microwave Nondestructive Testing Technique for Corrosion Under Insulation: A Review

Yee, Tan Shin; Shrifan, Nawaf H. M. M.; Al-Gburi, Ahmed Jamal Abdullah; Isa, Nor Ashidi Mat; Akbar, Muhammad Firdaus

doi:10.1109/access.2022.3197291

Cited by 10 publications

(5 citation statements)

References 146 publications

(138 reference statements)

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“…Initially, we address the threshold parameter α, which is used to define the ROI area from the radar image using Eq. (10). While we set this parameter to α = 0.05 in Sec.…”

Section: ) Sensitivity To Parameters and Additive Noisementioning

confidence: 99%

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Complex Permittivity Retrieval Approach With Radar Enhanced Contrast Source Inversion for Microwave Nondestructive Road Evaluation

Suzuki,

Nakamura,

Kidera

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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An experimental investigation of microwave quantitative imaging of nondestructive testing applications for concrete road structures is presented. A radar prior-based contrast source inversion (CSI) scheme is introduced to achieve an accurate reconstruction for target shape and dielectric property by applying the promising radar approach-range points migration (RPM). Moreover, to address the local optimum problem, we introduce an appropriate initial estimate for complex permittivity that focuses on the cost function of the CSI. A numerical and real data from an actual concrete road model, where a thin-layer water-filled cavity is buried into the boundary area between the asphalt and the concrete floorboard, show that our proposed approach improves reconstruction accuracy for permittivity and conductivity in a real nondestructive testing model.

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“…Initially, we address the threshold parameter α, which is used to define the ROI area from the radar image using Eq. (10). While we set this parameter to α = 0.05 in Sec.…”

Section: ) Sensitivity To Parameters and Additive Noisementioning

confidence: 99%

“…( 9) and the ROI limitation results in Eq. (10). In the RPM processing σ θ = 0.1 radian and σ X = 5 mm are set, and the parameter α = 0.05 in this case.…”

Section: ) Relative Permittivity Estimate Of Background Mediamentioning

confidence: 99%

Complex Permittivity Retrieval Approach With Radar Enhanced Contrast Source Inversion for Microwave Nondestructive Road Evaluation

Suzuki,

Nakamura,

Kidera

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In recent years, many scholars have employed machine learning techniques to further enhance the accuracy of NDT for thermal barrier coatings. Utilizing machine learning in NDT has further improved accuracy, enabling more efficient and precise evaluation of critical components such as TBCs in aerospace applications [25][26][27]. Therefore, combining a superior NDT method with machine learning techniques enables higher accuracy in detecting the TBCs' porosity.…”

Section: Introductionmentioning

confidence: 99%

Nondestructive Evaluation of Thermal Barrier Coatings’ Porosity Based on Terahertz Multi-Feature Fusion and a Machine Learning Approach

Zhang

et al. 2023

Applied Sciences

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Thermal barrier coatings (TBCs) play a crucial role in safeguarding aero-engine blades from high-temperature environments and enhancing their performance and durability. Accurate evaluation of TBCs’ porosity is of paramount importance for aerospace material research. However, existing evaluation methods often involve destructive testing or lack precision. In this study, we proposed a novel nondestructive evaluation method for TBCs’ porosity, utilizing terahertz time-domain spectroscopy (THz-TDS) and a machine learning approach. The primary objective was to achieve reliable and precise porosity evaluation without causing damage to the coatings. Multiple feature parameters were extracted from THz-TDS data to characterize porosity variations. Additionally, correlation analysis and p-value testing were employed to assess the significance and correlations among the feature parameters. Subsequently, the dung-beetle-optimizer-algorithm-optimized random forest (DBO-RF) regression model was applied to accurately predict the porosity. Model performance was evaluated using K-fold cross-validation. Experimental results demonstrated the effectiveness of our proposed method, with the DBO-RF model achieving high precision and robustness in porosity prediction. The model evaluation revealed a root-mean-square error of 1.802, mean absolute error of 1.549, mean absolute percentage error of 8.362, and average regression coefficient of 0.912. This study introduces a novel technique that presents a dependable nondestructive testing solution for the evaluation and prediction of TBCs’ porosity, effectively monitoring the service life of TBCs and determining their effectiveness. With its practical applicability in the aerospace industry, this method plays a vital role in the assessment and analysis of TBCs’ performance, driving progress in aerospace material research.

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“…When electrical current in the excitation coil is switched off, magnetic field drops suddenly generating eddy current measured by the receiver coil. [11,13] Since the diffusion of the eddy current depends on the wall thickness, pipe thinning and presence of defects can be detected. [1,11] The technique requires a highly skilled operator and is inadequate to detect localized corrosion as it averages the wall thickness over a relatively large area.…”

mentioning

confidence: 99%

“…Although the profile radiography is an efficient technique for pipe wall thickness evaluation, the overall measurement process is rather complex and entails careful and continuous control and supervision to ensure safe operation with the X-ray source. [1,6,11,13] Moreover, the diameter of the examined pipe is restricted to below 200 mm limiting the applicability to small structures, and the access to both sides of the inspected equipment is required. [2,6,12] Generally, the main drawback of techniques of the second group is their ability to provide only postfactum information after CUI has already occurred.…”

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confidence: 99%

Application of flexible resistometric sensors for real‐time corrosion monitoring under insulation

Popova,

Prošek,

Šefl

et al. 2024

Materials & Corrosion

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Corrosion under insulation (CUI) is a form of corrosion typically occurring on steel pipes and vessels covered with thermal insulation. CUI is a critical issue in petrochemical and refining industry leading to significant financial losses and severe environmental hazards. State‐of‐the art CUI monitoring system consisting of wireless loggers with flexible resistometric sensors is used to assess the effect of insulation material, position, and presence of a defect on the actual corrosivity toward carbon steel under alternating condensation conditions. All three considered factors together with the climatic parameters and presence of corrosion products are important for the CUI process. Most complex is the role of the insulation material as the properties inhibiting corrosion under intact insulation may be linked to a worse insulating ability. Because of the high sensitivity and immediate response of the sensors, the technique can be applied for timely CUI detection of insulated pipelines in both industrial facilities and laboratory studies.

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Prospect of Using Machine Learning-Based Microwave Nondestructive Testing Technique for Corrosion Under Insulation: A Review

Cited by 10 publications

References 146 publications

Complex Permittivity Retrieval Approach With Radar Enhanced Contrast Source Inversion for Microwave Nondestructive Road Evaluation

Complex Permittivity Retrieval Approach With Radar Enhanced Contrast Source Inversion for Microwave Nondestructive Road Evaluation

Nondestructive Evaluation of Thermal Barrier Coatings’ Porosity Based on Terahertz Multi-Feature Fusion and a Machine Learning Approach

Application of flexible resistometric sensors for real‐time corrosion monitoring under insulation

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