Composite such as Glass Fibre Reinforced Polymer (GFRP) is increasingly used as insulation in many industrial applications such as the steel pipelines in the oil and gas industry. Due to ageing and cyclic operation, many hidden defects exist under insulation, such as corrosion and delamination. If these defects are not promptly detected and restored, the growth of defects causes a catastrophic loss. Therefore, an effective inspection technique using non-destructive testing (NDT) to detect the underneath defect is required. The ability of microwave signals to penetrate and interact with the inner structure within composites makes them a promising candidate for composite inspection. In the case of GFRP, the random patterns cause permittivity variations that influence the propagation of the microwave signals, which results in a blurred spatial image making the assessment of the material's state difficult. In this research, a novel microwave NDT technique is presented based on k-means unsupervised machine learning for defect detection in composites. At present, the defect evaluation using an unsupervised machine learning-based microwave NDT technique is not reported elsewhere. The unsupervised machine learning is employed to enhance the imaging efficiency and defect detection in GFRP. The technique is based on scanning the composite material with an open-ended rectangular waveguide operating from 18 to 26.5 GHz with 101 frequency points. The influence of the permittivity variations on the reflected coefficients due to the random patterns of GFRP is mitigated by measuring the mean of a set of the adjacent points at each operating frequency point using a small rectangular window. The measured data is converted to the time domain using a fast inverse Fourier transform (IFFT) to provide significant features and increase the signal resolution to 201-time steps. K-means algorithm is utilized to cluster the given features into the defect and defect-free regions in GFRP. The findings presented in this paper demonstrate the benefits of an unsupervised machine learning to detect a defect down to 1 mm, which is a considerable contribution over any existing defect inspection technique in composites.

Alongside the development of high-speed rail, rail flaw detection is of great importance to ensure railway safety, especially for improving the speed and load of the train. Several conventional inspection methods such as visual, acoustic, and electromagnetic inspection have been introduced in the past. However, these methods have several challenges in terms of detection speed and accuracy. Combined inspection methods have emerged as a promising approach to overcome these limitations. Nondestructive testing (NDT) techniques in conjunction with artificial intelligence approaches have tremendous potential and viability because it is highly possible to improve the detection accuracy which has been proven in various conventional nondestructive testing techniques. With the development of information technology, communication technology, and sensor technology, rail health monitoring systems have been evolving, and have become equally significant and challenging because they can achieve real-time detection and give a risk warning forecast. This paper provides an in-depth review of traditional nondestructive techniques for rail inspection as well as the development of using machine learning approaches, combined nondestructive techniques, and rail health monitoring systems.

Recent years have witnessed an increase in the use of composite coatings for numerous applications, including aerospace, aircraft, and maritime vessels. These materials owe this popularity surge to the superior strength, weight, stiffness, and electrical insulation they exhibit over conventional substances, such as metals. The growing demand for such materials is accompanied by the inevitable need for fast, accurate, and affordable nondestructive testing techniques to reveal any possible defects within the coatings or any defects under coating. However, typical nondestructive testing (NDT) techniques such as ultrasonic testing (UT), infrared thermography (IRT), eddy current testing (ECT), and laser shearography (LS) have failed to provide successful results when inspecting composite coatings. Consequently, microwave NDT techniques have emerged to compensate for the shortcomings of traditional NDT approaches. Numerous microwave NDT methods have been reported for composite coatings inspection. Although existing microwave NDT methods have shown successful inspection of composite coatings, they often face several challenges, such as low spatial image quality and extensive data interpretation. Nevertheless, many of these limitations can be addressed by utilizing microwave NDT techniques with modern technologies such as soft computing. Artificially intelligent techniques have greatly enhanced the reliability and accuracy of microwave NDT techniques. This paper reviews various traditional NDT techniques and their limitations in inspecting composite coatings. In addition, the article includes a detailed review of several microwave NDT techniques and their benefits in evaluating composite coatings. The paper also highlights the advantages of using the recently reported microwave NDT approaches employing artificial intelligence approaches. This review demonstrates that microwave NDT techniques in conjunction with artificial intelligence approaches have excellent prospects for further enhancing composite coatings inspection and assessment efficiency. The review aimed to provide the reader with a comprehensive overview of most NDT techniques used for composite materials alongside their most salient features.

Microwave Non-Destructive Testing (NDT) techniques for dielectric coatings are vital processes in many industrial applications due to their superior defect detection capabilities over traditional NDT methods. However, the limited bandwidth and post-processing complexities of these techniques cause them to fall short of accurately estimating the delamination thickness in such structures. In this paper, a novel low-complexity signal processing method is proposed to estimate the delamination thickness in metal-backed dielectric coatings using bandwidth-limited Open-Ended Rectangular Waveguide (OERW) probes. Here, Inverse Fast Fourier Transform (IFFT) process is used to convert the measured complex reflection coefficients at the surface of the coating to the time domain. Next, the amplitude of a specific time step is used to accurately estimate the delamination thickness without extra time-consuming processing. Using a 3 mm-thick macor samples with machined defects, the proposed technique is validated by correctly estimating delamination thickness down to 0.4 mm using an OERW probe with 13.5 GHz bandwidth. Hence, this technique overcomes the challenge of the OERW's limited bandwidth, which highly affects the possibility of tracking shifting in the peak reflection in the time domain. Moreover, the proposed method does not add to the complexity of the NDT process, which makes it suitable for in-situ real time applications. Hence, following this approach would be of great importance to numerous industries, where accurate thickness estimation of minute delamination in coatings is essential to avoid system failure.

Carbon-fiber-reinforced polymer (CFRP) is widely acknowledged as a leading advanced material structure, offering superior properties compared to traditional materials, and has found diverse applications in several industrial sectors, such as that of automobiles, aircrafts, and power plants. However, the production of CFRP composites is prone to fabrication problems, leading to structural defects arising from cycling and aging processes. Identifying these defects at an early stage is crucial to prevent service issues that could result in catastrophic failures. Hence, routine inspection and maintenance are crucial to prevent system collapse. To achieve this objective, conventional nondestructive testing (NDT) methods are utilized to inspect CFRP components. However, the restricted field penetration within the CFRP makes conventional NDT approaches ineffective. Recently, microwave techniques have been developed to address the challenges associated with CFRP inspection by providing better material penetration and more precise results. This paper offers a review of the primary NDT methods employed to inspect CFRP composites, emphasizing microwave-based NDT techniques and their key features.

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.