Non-destructive testing (NDT) methods are critical for evaluating the structural integrity of and detecting defects in composite materials across industries such as aerospace and renewable energy. This review examines the recent trends and successful implementations of NDT approaches for composite materials, focusing on articles published between 2015 and 2025. A systematic literature review identified 120 relevant articles, highlighting techniques such as ultrasonic testing (UT), acoustic emission testing (AET), thermography (TR), radiographic testing (RT), eddy current testing (ECT), infrared thermography (IRT), X-ray computed tomography (XCT), and digital radiography testing (DRT). These methods effectively detect defects such as debonding, delamination, and voids in fiber-reinforced polymer (FRP) composites. The selection of NDT approaches depends on the material properties, defect types, and testing conditions. Although each technique has advantages and limitations, combining multiple NDT methods enhances the quality assessment of composite materials. This review provides insights into the capabilities and limitations of various NDT techniques and suggests future research directions for combining NDT methods to improve quality control in composite material manufacturing. Future trends include adopting multimodal NDT systems, integrating digital twin and Industry 4.0 technologies, utilizing embedded and wireless structural health monitoring, and applying artificial intelligence for automated defect interpretation. These advancements are promising for transforming NDT into an intelligent, predictive, and integrated quality assurance system.

On-stream inspections are the most appropriate method for routine inspections during plant operation without undergoing production downtime. Ultrasonic inspection, one of the on-stream inspection methods, faces challenges when performed at high temperatures exceeding the recommended 52 °C. This study aims to determine the ultrasonic velocity and attenuation with known material grade, thickness, and temperatures by comparing theoretical calculation and experimentation, with temperatures ranging between 30 °C to 250 °C on low-carbon steel, covering most petrochemical equipment material and working conditions. The aim of the theoretical analysis was to obtain Young’s modulus, Poisson’s ratio, and longitudinal velocity at different temperatures. The experiments validated the theoretical results of ultrasonic change due to temperature increase. It was found that the difference between the experiments and theoretical calculation is 3% at maximum. The experimental data of velocity and decibel change from the temperature range provide a reference for the future when dealing with unknown materials information on site that requires a quick corrosion status determination.

Petrochemical plants use on-stream inspection often to detect and monitor the corrosion on the equipment and piping system. Compared to ultrasonic thickness gauging and pulse-echo A-scan, phased array corrosion mapping has better coverability and can scan a large area to detect general and localized corrosion. This paper’s objective is to obtain documentary evidence for the accuracy of corrosion detection from 30 °C to 250 °C on A36 low-carbon steel by carrying out simulation experiments every 10 °C step. A minimum of three sets of phased array corrosion mapping data in each temperature were collected to study and evaluate the detectability. The data evidence could enhance the confidence level of the plant’s end users in using phased array mapping in the future during inspections. The experiments were found to be insufficiently thorough despite addressing the initial concerns, leaving more area for discussion in further studies, such as expanding the investigation to thicker carbon steel, stainless steel, and wedge materials.

The integrity of multi-bolted flanges is crucial for ensuring safety and operational efficiency in industrial systems across sectors such as oil and gas, chemical processing, and water treatment. Traditional non-destructive testing (NDT) methods often require operational downtime and may lack sensitivity for early-stage defect detection. This review examines acoustic emission testing (AET), a real-time monitoring technique for detecting acoustic waves generated by material defects. An analysis of 145 studies demonstrated AET’s effectiveness in detecting early-stage defects across various materials and industrial applications. Recent advances in sensor technology and signal processing have significantly enhanced AET’s capabilities. However, challenges remain regarding environmental noise interference and the need for specialized expertise. The review identifies knowledge gaps and proposes future research directions, including planned laboratory experiments to characterize defect signals in multi-bolted flange systems under different operational conditions. The findings position AET as a transformative solution for industrial inspection and maintenance, offering enhanced safety and reliability for critical infrastructures.

Fiber-reinforced polymer (FRP) pipes have emerged as a preferred alternative to conventional metallic piping systems in various industries, including chemical processing, marine, and oil and gas industries, owing to their superior corrosion resistance, high strength-to-weight ratio, and extended service life. However, ensuring the long-term reliability and structural integrity of FRP pipes presents significant challenges, primarily because of their anisotropic and heterogeneous nature, which complicates defect detection and characterization. Traditional non-destructive testing (NDT) methods, which are widely applied, often fail to address these complexities, necessitating the adoption of advanced digital techniques. This review systematically examines recent advancements in digital NDT approaches with a particular focus on their application to composite materials. Drawing from 140 peer-reviewed articles published between 2016 and 2024, this review highlights the role of numerical modeling, simulation, machine learning (ML), and deep learning (DL) in enhancing defect detection sensitivity, automating data interpretation, and supporting predictive maintenance strategies. Numerical techniques, such as the finite element method (FEM) and Monte Carlo simulations, have been shown to improve inspection reliability through virtual defect modeling and parameter optimization. Meanwhile, ML and DL algorithms demonstrate transformative capabilities in automating defect classification, segmentation, and severity assessment, significantly reducing the inspection time and human dependency. Despite these promising developments, this review identifies a critical gap in the field: the limited translation of advanced digital methods into field-deployable solutions specifically tailored for FRP piping systems. The unique structural complexities and operational demands of FRP pipes require dedicated research for the development of validated digital models, application-specific datasets, and industry-aligned evaluation protocols. This review provides strategic insights and future research directions aimed at bridging the gap and promoting the integration of digital NDT technologies into real-world FRP pipe inspection and lifecycle management frameworks.

Safe and efficient operation of port cranes is vital for maintaining the efficiency of global maritime logistics. However, traditional non-destructive testing methods face significant limitations in harsh port environments, such as periodic inspection intervals, restricted access to structural components, and a lack of real-time monitoring. This review explores the emerging paradigm of remote non-destructive testing through the integration of vibration and acoustic emission sensors with Internet of Things platforms. By enabling continuous, real-time monitoring, these sensor systems can detect early indicators of mechanical degradation, structural fatigue, and corrosion. This study synthesizes findings from over 100 peer-reviewed sources and identifies a significant gap in the application of these technologies to port cranes. Although vibration and acoustic emission sensors have been widely studied in various fields, their application to port cranes remains underexplored, presenting a novel and promising avenue for future research and practical applications. The unique operational demands and structural complexities of port cranes, coupled with their critical role in global trade logistics, make them ideal for leveraging these sensors in tandem with Internet of Things solutions. This integration not only overcomes the limitations of traditional non-destructive testing methods, but also offers substantial benefits, including enhanced safety, reduced inspection costs, and improved operational efficiency. This review concludes by proposing future research directions to enhance sensor performance, data analytics, and Internet of Things integration, paving the way for predictive maintenance strategies that increase operational uptime and improve safety in port crane operations.