Advancements in and Research on Coplanar Capacitive Sensing Techniques for Non-Destructive Testing and Evaluation: A State-of-the-Art Review.

In contrast to conventional non-destructive testing (NDT) and non-destructive evaluation (NDE) methodologies, including radiography, ultrasound, and eddy current analysis, coplanar capacitive sensing technique emerges as a novel and promising avenue within the field. This paper endeavors to elucidate the efficacy of coplanar capacitive sensing, also referred to as capacitive imaging (CI), within the realm of NDT. Leveraging extant scholarly discourse, this review offers a comprehensive and methodical examination of the coplanar capacitive technique, encompassing its fundamental principles, factors influencing sensor efficacy, and diverse applications for defect identification across various NDT domains.

A Complementary Fusion-Based Multimodal Non-Destructive Testing and Evaluation Using Phased-Array Ultrasonic and Pulsed Thermography on a Composite Structure.

Combinative methodologies have the potential to address the drawbacks of unimodal non-destructive testing and evaluation (NDT & E) when inspecting multilayer structures. The aim of this study is to investigate the integration of information gathered via phased-array ultrasonic testing (PAUT) and pulsed thermography (PT), addressing the challenges posed by surface-level anomalies in PAUT and the limited deep penetration in PT. A center-of-mass-based registration method was proposed to align shapeless inspection results in consecutive insertions.

Robust Multi-Modal Image Registration for Image Fusion Enhancement in Infrastructure Inspection.

Efficient multi-modal image fusion plays an important role in the non-destructive evaluation (NDE) of infrastructures, where an essential challenge is the precise visualizing of defects. While automatic defect detection represents a significant advancement, the determination of the precise location of both surface and subsurface defects simultaneously is crucial. Hence, visible and infrared data fusion strategies are essential for acquiring comprehensive and complementary information to detect defects across vast structures.

Advanced Image Stitching Method for Dual-Sensor Inspection.

Efficient image stitching plays a vital role in the Non-Destructive Evaluation (NDE) of infrastructures. An essential challenge in the NDE of infrastructures is precisely visualizing defects within large structures. The existing literature predominantly relies on high-resolution close-distance images to detect surface or subsurface defects.

Simulation-aided infrared thermography with decomposition-based noise reduction for detecting defects in ancient polyptychs.

In recent years, the conservation and protection of ancient cultural heritage have received increasing attention, and non-destructive testing (NDT), which can minimize the damage done to the test subject, plays an integral role therein. For instance, NDT through active infrared thermal imaging can be applied to ancient polyptychs, which can realize accurate detection of damage and defects existing on the surface and interior of the polyptychs. In this study, infrared thermography is used for non-invasive investigation and evaluation of two polyptych samples with different pigments and artificial defects, but both reproduced based on a painting by Pietro Lorenzetti (1280/85-1348) using the typical tempera technique of the century.

Automatic Detection and Identification of Defects by Deep Learning Algorithms from Pulsed Thermography Data.

Infrared thermography (IRT), is one of the most interesting techniques to identify different kinds of defects, such as delamination and damage existing for quality management of material. Objective detection and segmentation algorithms in deep learning have been widely applied in image processing, although very rarely in the IRT field. In this paper, spatial deep-learning image processing methods for defect detection and identification were discussed and investigated.

Deep Learning Neural Network Performance on NDT Digital X-ray Radiography Images: Analyzing the Impact of Image Quality Parameters-An Experimental Study.

In response to the growing inspection demand exerted by process automation in component manufacturing, non-destructive testing (NDT) continues to explore automated approaches that utilize deep-learning algorithms for defect identification, including within digital X-ray radiography images. This necessitates a thorough understanding of the implication of image quality parameters on the performance of these deep-learning models. This study investigated the influence of two image-quality parameters, namely signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), on the performance of a U-net deep-learning semantic segmentation model.

Defect Detection and Depth Estimation in Composite Materials for Pulsed Thermography Images by Nonuniform Heating Correction and Oriented Gradient Information.

Pulsed thermography is a nondestructive method commonly used to explore anomalies in composite materials. This paper presents a procedure for the automated detection of defects in thermal images of composite materials obtained with pulsed thermography experiments. The proposed methodology is simple and novel as it is reliable in low-contrast and nonuniform heating conditions and does not require data preprocessing.

Experimental Study and FEM Simulations for Detection of Rebars in Concrete Slabs by Coplanar Capacitive Sensing Technique.

In the present study, a relatively novel non-destructive testing (NDT) method called the coplanar capacitive sensing technique was applied in order to detect different sizes of rebars in a reinforced concrete (RC) structure. This technique effectively detects changes in the dielectric properties during scanning in various sections of concrete with and without rebars. Numerical simulations were carried out by three-dimensional (3D) finite element modelling (FEM) in COMSOL Multiphysics software to analyse the impact of the presence of rebars on the electric field generated by the coplanar capacitive probe.

Dual-Intended Deep Learning Model for Breast Cancer Diagnosis in Ultrasound Imaging.

Automated medical data analysis demonstrated a significant role in modern medicine, and cancer diagnosis/prognosis to achieve highly reliable and generalizable systems. In this study, an automated breast cancer screening method in ultrasound imaging is proposed. A convolutional deep autoencoder model is presented for simultaneous segmentation and radiomic extraction.

Data Enhancement via Low-Rank Matrix Reconstruction in Pulsed Thermography for Carbon-Fibre-Reinforced Polymers.

Pulsed thermography is a commonly used non-destructive testing method and is increasingly studied for the assessment of advanced materials such as carbon fibre-reinforced polymer (CFRP). Different processing approaches are proposed to detect and characterize anomalies that may be generated in structures during the manufacturing cycle or service period. In this study, matrix decomposition using Robust PCA via Inexact-ALM is investigated as a pre- and post-processing approach in combination with state-of-the-art approaches (i.

Multi-Excitation Infrared Fusion for Impact Evaluation of Aluminium-BFRP/GFRP Hybrid Composites.

Fibre metal laminates are widely implemented in the aerospace industry owing to the merits of fatigue resistance and plastic properties. An effective defect assessment technique needs to be investigated for this type of composite materials. In order to achieve accurate impact-induced damage evaluation, a multi-excitation infrared fusion method is introduced in this study.

Impartially Validated Multiple Deep-Chain Models to Detect COVID-19 in Chest X-ray Using Latent Space Radiomics.

The COVID-19 pandemic continues to spread globally at a rapid pace, and its rapid detection remains a challenge due to its rapid infectivity and limited testing availability. One of the simply available imaging modalities in clinical routine involves chest X-ray (CXR), which is often used for diagnostic purposes. Here, we proposed a computer-aided detection of COVID-19 in CXR imaging using deep and conventional radiomic features.

Multiscale Analysis of Solar Loading Thermographic Signals for Wall Structure Inspection.

Infrared thermography has been widely adopted in many applications for material structure inspection, where data analysis methods are often implemented to elaborate raw thermal data and to characterize material structural properties. Herein, a multiscale thermographic data analysis framework is proposed and applied to building structure inspection. In detail, thermograms are first collected by conducting solar loading thermography, which are then decomposed into several intrinsic mode functions under different spatial scales by multidimensional ensemble empirical mode decomposition.

Robust Principal Component Thermography for Defect Detection in Composites.

Pulsed Thermography (PT) data are usually affected by noise and as such most of the research effort in the last few years has been directed towards the development of advanced signal processing methods to improve defect detection. Among the numerous techniques that have been proposed, principal component thermography (PCT)-based on principal component analysis (PCA)-is one of the most effective in terms of defect contrast enhancement and data compression. However, it is well-known that PCA can be significantly affected in the presence of corrupted data (e.

Evaluation and Selection of Video Stabilization Techniques for UAV-Based Active Infrared Thermography Application.

Unmanned Aerial Vehicles (UAVs) that can fly around an aircraft carrying several sensors, e.g., thermal and optical cameras, to inspect the parts of interest without removing them can have significant impact in reducing inspection time and cost.

Introduction of Deep Learning in Thermographic Monitoring of Cultural Heritage and Improvement by Automatic Thermogram Pre-Processing Algorithms.

The monitoring of heritage objects is necessary due to their continuous deterioration over time. Therefore, the joint use of the most up-to-date inspection techniques with the most innovative data processing algorithms plays an important role to apply the required prevention and conservation tasks in each case study. InfraRed Thermography (IRT) is one of the most used Non-Destructive Testing (NDT) techniques in the cultural heritage field due to its advantages in the analysis of delicate objects (i.

Enhanced Infrared Sparse Pattern Extraction and Usage for Impact Evaluation of Basalt-Carbon Hybrid Composites by Pulsed Thermography.

Nowadays, infrared thermography, as a widely used non-destructive testing method, is increasingly studied for impact evaluation of composite structures. Sparse pattern extraction is attracting increasing attention as an advanced post-processing method. In this paper, an enhanced sparse pattern extraction framework is presented for thermographic sequence processing and defect detection.

Detecting Vasodilation as Potential Diagnostic Biomarker in Breast Cancer Using Deep Learning-Driven Thermomics.

Breast cancer is the most common cancer in women. Early diagnosis improves outcome and survival, which is the cornerstone of breast cancer treatment. Thermography has been utilized as a complementary diagnostic technique in breast cancer detection.

Thermal imaging dataset from composite material academic samples inspected by pulsed thermography.

This paper presents a thermal imaging dataset from composite material samples (carbon and glass fiber reinforced plastic) that were inspected by pulsed thermography with the goal of detecting and characterizing subsurface defective zones (Teflon inserts representing delaminations between plies). The pulsed thermography experiment was applied to 6 academic plates (inspected from both sides) all having the dimensions of 300 mm x 300 mm x 2 mm and same distribution of defects but made of different materials: three plates on carbon fiber-reinforced plastic (CFRP) and three plates made on glass fiber reinforced plastic (GFRP) specimens with three different geometries: planar, curved and trapezoidal. Each plate contains 25 inserts having length/depth ratios between 1.

Comparison of Cooled and Uncooled IR Sensors by Means of Signal-to-Noise Ratio for NDT Diagnostics of Aerospace Grade Composites.

This work aims to address the effectiveness and challenges of non-destructive testing (NDT) by active infrared thermography (IRT) for the inspection of aerospace-grade composite samples and seeks to compare uncooled and cooled thermal cameras using the signal-to-noise ratio (SNR) as a performance parameter. It focuses on locating impact damages and optimising the results using several signal processing techniques. The work successfully compares both types of cameras using seven different SNR definitions, to understand if a lower-resolution uncooled IR camera can achieve an acceptable NDT standard.

Validation of Anatomical Sites for the Measurement of Infrared Body Surface Temperature Variation in Response to Handling and Transport.

This study aimed at validating the anatomical sites for the measurement of infrared (IR) body surface temperature as a tool to monitor the pigs' response to handling and transport stress. The selected anatomical sites were the neck (infrared neck temperature-IRNT), rump (infrared rump temperature-IRRT), orbital (infrared orbital temperature-IROT) and behind ears (infrared behind ears temperature-IRBET) regions. A total of 120 pigs were handled from the finishing pen to the loading dock through a handling test course.

Continuum removal for ground-based LWIR hyperspectral infrared imagery applying non-negative matrix factorization.

Continuum removal is vital in hyperspectral image analysis. It enables data to be used for any application and usually requires approximations or assumptions to be made. One of these approximations is related to the calculation of the spectra of the background's blackbody temperature.

Eddy current pulsed thermography for ballistic impact evaluation in basalt-carbon hybrid composite panels.

In this paper, eddy current pulsed thermography was used to evaluate ballistic impact damages in basalt-carbon hybrid fiber-reinforced polymer composite laminates for the first time, to our knowledge. In particular, different hybrid structures including intercalated stacking and sandwich-like sequences were used. Pulsed phase thermography, wavelet transform, principle component thermography, and partial least-squares thermography were used to process the thermographic data.

Atmospheric Carbon Mineralization in an Industrial-Scale Chrysotile Mining Waste Pile.

Magnesium-rich minerals that are abundant in ultramafic mining waste have the potential to be used as a safe and permanent sequestration solution for carbon dioxide (CO). Our understanding of thermo-hydro-chemical regimes that govern this reaction at an industrial scale, however, has remained an important challenge to its widespread implementation. Through a year-long monitoring experiment performed at a 110 Mt chrysotile waste pile, we have documented the existence of two distinct thermo-hydro-chemical regimes that control the ingress of CO and the subsequent mineral carbonation of the waste.