Defects Detection Using Complementary Split Ring Resonator with Microstrip Patch Antenna

Yee, Tan Shin; Akbar, M.S.; Ghazali, Nor Azlin; Mohamed, Mohamed Fauzi Packeer

doi:10.1007/978-981-16-8129-5_95

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…WT splits a stream of data into numerous levels reflecting distinct bandwidths [31,132]. The position of the WT at each scale determines the electrical noise.…”

Section: A Pre-processingmentioning

confidence: 99%

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

et al. 2022

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“…WT splits a stream of data into numerous levels reflecting distinct bandwidths [31,132]. The position of the WT at each scale determines the electrical noise.…”

Section: A Pre-processingmentioning

confidence: 99%

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

et al. 2022

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“…The detection of defects under ceramic coatings is difficult using conventional NDT techniques, including eddy current, thermography, and ultrasonic approaches, owing to field penetration restrictions when evaluating dielectric materials [3,4]. Due to the absence of induced currents, the eddy currents inspection method cannot be used to examine lossless dielectric materials [5].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Defects under Insulation Using K-Medoids Clustering Algorithm-Based Microwave Nondestructive Testing

et al. 2022

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Composite insulations, such as ceramics, are commonly utilized in the turbine system as a thermal coating barrier to protect the metal substrate against high temperatures and pressure. The presence of delamination in the composite insulations may cause turbine failure, leading to a catastrophic accident. Thus, regular non-destructive testing is required to detect and evaluate insulation defects. Among the non-destructive testing techniques, the microwave technique has emerged as a promising method for assessing defects in ceramic coatings. Although the method is promising, microwave non-destructive testing suffers from poor spatial imaging, making the defect assessment challenging. In this paper, a novel technique based on microwave non-destructive testing with a k-medoids clustering algorithm for delamination detection is proposed. The representative ceramic coating sample is scanned using a Q-band open-ended rectangular waveguide with 101 frequency points that operated between 33 to 50 GHz. The measured data is transformed from the frequency domain to the time domain using an inverse fast Fourier transform. The principal component analysis is then used to reduce the dimensionality of 101 time steps into only 3 dominant attributes. The attributes of each inspected location are classified as defect or defect-free using the k-medoids clustering algorithm for accurately detecting and sizing the defects in the ceramic insulation. The results reported in this paper highlight the superiority of the k-medoids clustering algorithm in delamination detection, with an accuracy rate of 95.4%. This is a significant step forward compared to earlier approaches for identifying ceramic defects.

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Microwave Non-destructive Testing Using K-Medoids Clustering Algorithm

Yee,

Akbar,

Ghazali

et al. 2024

Lecture Notes in Electrical Engineering

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Defects Detection Using Complementary Split Ring Resonator with Microstrip Patch Antenna

Cited by 4 publications

References 7 publications

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

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

Assessment of Defects under Insulation Using K-Medoids Clustering Algorithm-Based Microwave Nondestructive Testing

Microwave Non-destructive Testing Using K-Medoids Clustering Algorithm

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